{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413075", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003747_gn_m1_14_87_m2_3_63_mtotal_18_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413064", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000627_gn_m1_93_45_m2_10_05_mtotal_103_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413112", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003648_cbc_m1_88_55_m2_9_95_mtotal_98_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413171", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007669_gn_m1_56_24_m2_43_26_mtotal_99_q_1_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002936_cbc_m1_69_69_m2_25_81_mtotal_95_q_2_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413083", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004801_gn_m1_34_8_m2_8_7_mtotal_43_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413200", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007275_gn_m1_35_3_m2_4_2_mtotal_39_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413198", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006939_gn_m1_26_04_m2_14_46_mtotal_40_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413188", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006120_cbc_m1_109_26_m2_35_24_mtotal_144_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413268", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008778_cbc_m1_6_87_m2_1_63_mtotal_8_q_4_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413280", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004613_gn_m1_63_0_m2_52_5_mtotal_115_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413277", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008401_gn_m1_130_74_m2_13_76_mtotal_144_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413267", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009674_cbc_m1_71_0_m2_35_5_mtotal_106_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413309", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009256_cbc_m1_112_12_m2_18_38_mtotal_130_q_6_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413346", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007773_gn_m1_54_0_m2_22_5_mtotal_76_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413317", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002411_gn_m1_86_35_m2_14_15_mtotal_100_q_6_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413343", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005990_cbc_m1_31_91_m2_3_59_mtotal_35_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413394", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006086_cbc_m1_36_56_m2_4_94_mtotal_41_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413434", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007402_cbc_m1_62_33_m2_9_17_mtotal_71_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413498", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007321_gn_m1_98_95_m2_14_55_mtotal_113_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413446", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008607_gn_m1_114_48_m2_22_02_mtotal_136_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413475", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006304_cbc_m1_50_69_m2_29_81_mtotal_80_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413519", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008991_gn_m1_124_24_m2_17_26_mtotal_141_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413518", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006212_cbc_m1_63_75_m2_63_75_mtotal_127_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413574", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009164_cbc_m1_69_59_m2_7_91_mtotal_77_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413601", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009820_cbc_m1_60_15_m2_11_35_mtotal_71_q_5_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413609", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002738_cbc_m1_12_19_m2_1_31_mtotal_13_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008287_gn_m1_10_16_m2_5_34_mtotal_15_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413641", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008167_gn_m1_25_5_m2_5_0_mtotal_30_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413657", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002421_gn_m1_98_99_m2_11_51_mtotal_110_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413694", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008106_cbc_m1_24_52_m2_5_98_mtotal_30_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413726", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004357_gn_m1_53_03_m2_7_47_mtotal_60_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413725", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007291_gn_m1_20_12_m2_14_38_mtotal_34_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413731", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004154_cbc_m1_130_95_m2_19_55_mtotal_150_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413740", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007889_gn_m1_99_67_m2_49_83_mtotal_149_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413764", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004656_cbc_m1_55_79_m2_50_71_mtotal_106_q_1_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413762", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003803_gn_m1_89_88_m2_17_62_mtotal_107_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413795", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007171_gn_m1_124_79_m2_13_71_mtotal_138_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413807", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004108_cbc_m1_65_35_m2_18_15_mtotal_83_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413852", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009546_cbc_m1_31_02_m2_3_48_mtotal_34_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413883", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009614_cbc_m1_21_51_m2_2_99_mtotal_24_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413922", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008003_gn_m1_59_01_m2_6_49_mtotal_65_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413957", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004603_gn_m1_92_17_m2_26_33_mtotal_118_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003361_gn_m1_82_73_m2_21_77_mtotal_104_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002137_gn_m1_18_71_m2_7_79_mtotal_26_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414030", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004624_cbc_m1_8_54_m2_0_96_mtotal_9_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414020", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007623_gn_m1_119_25_m2_13_25_mtotal_132_q_9_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3413988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007135_gn_m1_123_81_m2_17_69_mtotal_141_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414044", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005514_cbc_m1_94_59_m2_13_91_mtotal_108_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414043", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003558_cbc_m1_53_94_m2_5_56_mtotal_59_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414069", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003857_gn_m1_36_39_m2_10_11_mtotal_46_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414063", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001476_cbc_m1_63_75_m2_18_75_mtotal_82_q_3_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414082", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001715_gn_m1_63_88_m2_11_62_mtotal_75_q_5_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414083", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003066_cbc_m1_131_81_m2_16_69_mtotal_148_q_7_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414088", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007788_cbc_m1_126_82_m2_12_68_mtotal_139_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414125", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002230_cbc_m1_102_52_m2_26_98_mtotal_129_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414155", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002171_gn_m1_80_09_m2_17_41_mtotal_97_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414123", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006838_cbc_m1_126_36_m2_19_14_mtotal_145_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414096", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009747_gn_m1_80_22_m2_10_28_mtotal_90_q_7_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414173", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005039_gn_m1_14_05_m2_1_45_mtotal_15_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414183", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004309_gn_m1_77_57_m2_12_93_mtotal_90_q_6_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414197", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008154_cbc_m1_106_81_m2_16_69_mtotal_123_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414186", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003010_cbc_m1_13_04_m2_1_46_mtotal_14_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414211", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001181_gn_m1_12_83_m2_11_67_mtotal_24_q_1_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414215", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008492_cbc_m1_76_99_m2_13_51_mtotal_90_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414246", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004452_cbc_m1_96_59_m2_43_91_mtotal_140_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414230", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009024_cbc_m1_96_43_m2_16_07_mtotal_112_q_6_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414217", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004399_gn_m1_71_88_m2_15_63_mtotal_87_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414283", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008342_cbc_m1_116_32_m2_18_18_mtotal_134_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414286", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007616_cbc_m1_69_35_m2_14_15_mtotal_83_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414285", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009783_gn_m1_49_88_m2_35_62_mtotal_85_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414302", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001929_gn_m1_95_14_m2_41_36_mtotal_136_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414316", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003553_gn_m1_18_14_m2_2_36_mtotal_20_q_7_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000109_gn_m1_126_84_m2_15_66_mtotal_142_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414468", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006621_gn_m1_31_86_m2_6_64_mtotal_38_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414435", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002002_cbc_m1_91_05_m2_14_45_mtotal_105_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414471", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006794_cbc_m1_40_38_m2_4_12_mtotal_44_q_9_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414457", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001245_gn_m1_88_67_m2_15_83_mtotal_104_q_5_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414532", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007213_gn_m1_20_11_m2_2_39_mtotal_22_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414529", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004446_cbc_m1_7_56_m2_1_94_mtotal_9_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414556", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008705_gn_m1_83_25_m2_11_25_mtotal_94_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414577", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009697_gn_m1_36_26_m2_8_24_mtotal_44_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414584", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005070_cbc_m1_112_14_m2_15_36_mtotal_127_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414570", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006179_gn_m1_22_77_m2_3_73_mtotal_26_q_6_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414586", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005147_gn_m1_71_59_m2_15_91_mtotal_87_q_4_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414590", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003035_gn_m1_121_93_m2_12_57_mtotal_134_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414580", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005392_cbc_m1_66_27_m2_55_23_mtotal_121_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414606", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007760_cbc_m1_31_64_m2_3_86_mtotal_35_q_8_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414617", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007318_cbc_m1_19_97_m2_2_53_mtotal_22_q_7_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414599", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006867_gn_m1_107_11_m2_11_39_mtotal_118_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414595", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000570_cbc_m1_85_68_m2_13_82_mtotal_99_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414625", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007964_cbc_m1_91_78_m2_41_72_mtotal_133_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414688", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009311_gn_m1_13_82_m2_1_68_mtotal_15_q_8_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414784", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007534_cbc_m1_88_65_m2_9_85_mtotal_98_q_9_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414804", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004550_cbc_m1_118_58_m2_28_92_mtotal_147_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414811", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006101_gn_m1_97_76_m2_10_74_mtotal_108_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005218_cbc_m1_75_9_m2_14_6_mtotal_90_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414831", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002962_cbc_m1_59_46_m2_8_04_mtotal_67_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414879", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004896_cbc_m1_84_66_m2_9_84_mtotal_94_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414865", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007756_cbc_m1_10_45_m2_2_05_mtotal_12_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414848", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009088_cbc_m1_132_81_m2_13_69_mtotal_146_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414889", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008844_cbc_m1_21_24_m2_2_26_mtotal_23_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414878", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006998_cbc_m1_76_75_m2_7_75_mtotal_84_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414826", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000418_cbc_m1_44_83_m2_4_67_mtotal_49_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003239_gn_m1_47_4_m2_5_1_mtotal_52_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3414971", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000100_cbc_m1_41_52_m2_12_98_mtotal_54_q_3_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415019", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004278_cbc_m1_39_6_m2_9_9_mtotal_49_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415021", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008140_cbc_m1_64_57_m2_17_93_mtotal_82_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415023", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009686_cbc_m1_53_09_m2_10_41_mtotal_63_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000757_gn_m1_29_31_m2_4_19_mtotal_33_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415038", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007263_gn_m1_110_77_m2_12_73_mtotal_123_q_8_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415110", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001460_cbc_m1_79_86_m2_16_64_mtotal_96_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415126", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005112_cbc_m1_28_93_m2_3_57_mtotal_32_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415184", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008082_cbc_m1_53_7_m2_35_8_mtotal_89_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415165", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008043_gn_m1_55_47_m2_6_03_mtotal_61_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415218", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007479_gn_m1_97_5_m2_39_0_mtotal_136_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415268", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006912_cbc_m1_16_84_m2_3_66_mtotal_20_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415296", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005948_cbc_m1_115_93_m2_17_57_mtotal_133_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415309", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006139_gn_m1_80_55_m2_8_95_mtotal_89_q_9_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415327", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006897_gn_m1_115_85_m2_24_65_mtotal_140_q_4_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415351", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006238_cbc_m1_17_59_m2_1_91_mtotal_19_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415367", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000877_gn_m1_52_5_m2_35_0_mtotal_87_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415390", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007599_gn_m1_4_64_m2_1_86_mtotal_6_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415411", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001400_cbc_m1_54_65_m2_18_85_mtotal_73_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415410", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002493_gn_m1_62_04_m2_34_46_mtotal_96_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415416", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004567_gn_m1_15_7_m2_2_8_mtotal_18_q_5_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415456", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006779_gn_m1_38_77_m2_4_73_mtotal_43_q_8_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415449", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006458_cbc_m1_48_82_m2_40_68_mtotal_89_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415467", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004688_cbc_m1_29_85_m2_7_65_mtotal_37_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415388", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003528_cbc_m1_6_79_m2_2_71_mtotal_9_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415540", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007619_gn_m1_69_35_m2_14_15_mtotal_83_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415584", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004503_gn_m1_41_36_m2_4_14_mtotal_45_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415578", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002515_gn_m1_13_7_m2_2_8_mtotal_16_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415607", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004021_gn_m1_69_1_m2_14_4_mtotal_83_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415613", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001351_gn_m1_31_75_m2_31_75_mtotal_63_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415636", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001429_gn_m1_98_33_m2_49_17_mtotal_147_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415665", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001994_cbc_m1_31_41_m2_13_09_mtotal_44_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415673", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008462_cbc_m1_10_21_m2_7_29_mtotal_17_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415687", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000076_cbc_m1_114_14_m2_25_36_mtotal_139_q_4_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415729", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008894_cbc_m1_47_13_m2_6_37_mtotal_53_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415705", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002217_gn_m1_7_01_m2_1_49_mtotal_8_q_4_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415733", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001427_gn_m1_21_19_m2_7_31_mtotal_28_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415767", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006452_cbc_m1_111_15_m2_16_35_mtotal_127_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415782", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000648_cbc_m1_71_2_m2_11_3_mtotal_82_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415814", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003564_cbc_m1_101_5_m2_35_0_mtotal_136_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415790", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008993_gn_m1_124_24_m2_17_26_mtotal_141_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415749", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007724_cbc_m1_9_41_m2_4_09_mtotal_13_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415775", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000471_gn_m1_4_55_m2_0_95_mtotal_5_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415765", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004203_gn_m1_93_0_m2_46_5_mtotal_139_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415812", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004346_cbc_m1_106_37_m2_13_13_mtotal_119_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415779", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005677_gn_m1_33_75_m2_18_75_mtotal_52_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415818", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009157_gn_m1_19_55_m2_1_95_mtotal_21_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415839", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006692_cbc_m1_51_54_m2_6_96_mtotal_58_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415825", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005471_gn_m1_4_91_m2_2_59_mtotal_7_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415868", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002629_gn_m1_39_51_m2_3_99_mtotal_43_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415874", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006422_cbc_m1_64_02_m2_30_48_mtotal_94_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415966", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004530_cbc_m1_43_15_m2_6_35_mtotal_49_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416002", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006556_cbc_m1_86_49_m2_9_01_mtotal_95_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3415984", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000268_cbc_m1_51_69_m2_5_81_mtotal_57_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416025", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002203_gn_m1_91_09_m2_31_41_mtotal_122_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009493_gn_m1_16_93_m2_4_57_mtotal_21_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416024", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000375_gn_m1_35_65_m2_6_85_mtotal_42_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416035", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002376_cbc_m1_26_26_m2_3_24_mtotal_29_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416093", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006836_cbc_m1_126_36_m2_19_14_mtotal_145_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416149", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005628_cbc_m1_49_77_m2_8_73_mtotal_58_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416159", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007702_cbc_m1_51_25_m2_6_25_mtotal_57_q_8_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416163", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000053_gn_m1_22_6_m2_11_9_mtotal_34_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416215", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003387_gn_m1_66_5_m2_19_0_mtotal_85_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416255", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002053_gn_m1_66_29_m2_9_21_mtotal_75_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416379", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002272_cbc_m1_103_04_m2_17_46_mtotal_120_q_5_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416382", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004478_cbc_m1_115_86_m2_19_64_mtotal_135_q_5_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416368", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002470_cbc_m1_32_45_m2_27_05_mtotal_59_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416393", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008781_gn_m1_6_87_m2_1_63_mtotal_8_q_4_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416404", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007487_gn_m1_40_95_m2_21_55_mtotal_62_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416396", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006255_gn_m1_6_07_m2_2_43_mtotal_8_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416517", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007689_gn_m1_41_46_m2_23_04_mtotal_64_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416553", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008538_cbc_m1_86_54_m2_13_96_mtotal_100_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416564", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000900_cbc_m1_77_43_m2_8_07_mtotal_85_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416476", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003853_gn_m1_36_39_m2_10_11_mtotal_46_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416527", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003253_gn_m1_40_09_m2_33_41_mtotal_73_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416516", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005311_gn_m1_103_73_m2_35_77_mtotal_139_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416573", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007292_cbc_m1_20_12_m2_14_38_mtotal_34_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005857_gn_m1_92_49_m2_11_01_mtotal_103_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416417", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000476_cbc_m1_6_27_m2_5_23_mtotal_11_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416703", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003342_cbc_m1_15_62_m2_4_88_mtotal_20_q_3_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416627", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003526_cbc_m1_93_74_m2_40_76_mtotal_134_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416745", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001416_cbc_m1_31_35_m2_6_15_mtotal_37_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416772", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004164_cbc_m1_26_14_m2_11_36_mtotal_37_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416819", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007103_gn_m1_99_35_m2_26_15_mtotal_125_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416821", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002510_cbc_m1_95_71_m2_16_79_mtotal_112_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003013_gn_m1_72_97_m2_15_53_mtotal_88_q_4_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416837", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005506_cbc_m1_120_75_m2_28_75_mtotal_149_q_4_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416857", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002925_gn_m1_67_4_m2_32_1_mtotal_99_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416864", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007238_cbc_m1_50_47_m2_24_03_mtotal_74_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005227_gn_m1_75_55_m2_10_95_mtotal_86_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416983", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001521_gn_m1_110_9_m2_12_6_mtotal_123_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3416992", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006314_cbc_m1_102_82_m2_11_68_mtotal_114_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417011", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009393_gn_m1_53_94_m2_8_56_mtotal_62_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417029", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002733_gn_m1_18_38_m2_6_12_mtotal_24_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417013", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000943_gn_m1_50_42_m2_10_08_mtotal_60_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417041", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003325_gn_m1_95_56_m2_11_94_mtotal_107_q_8_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417074", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000749_gn_m1_78_93_m2_31_57_mtotal_110_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417053", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000644_cbc_m1_45_65_m2_19_85_mtotal_65_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417024", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005590_cbc_m1_47_2_m2_14_3_mtotal_61_q_3_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417100", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000382_cbc_m1_96_31_m2_13_19_mtotal_109_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417114", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007965_gn_m1_91_78_m2_41_72_mtotal_133_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004062_cbc_m1_127_72_m2_18_78_mtotal_146_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417153", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000735_gn_m1_22_92_m2_2_58_mtotal_25_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417170", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009051_gn_m1_109_86_m2_16_64_mtotal_126_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417213", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008028_cbc_m1_11_53_m2_3_97_mtotal_15_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417245", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007100_cbc_m1_99_35_m2_26_15_mtotal_125_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417261", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002874_cbc_m1_99_11_m2_13_39_mtotal_112_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417287", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001168_cbc_m1_75_69_m2_68_81_mtotal_144_q_1_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417374", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008581_gn_m1_58_5_m2_9_0_mtotal_67_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417369", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003772_cbc_m1_25_76_m2_10_74_mtotal_36_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417379", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004549_gn_m1_118_58_m2_28_92_mtotal_147_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417393", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009830_cbc_m1_5_81_m2_0_69_mtotal_6_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417412", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006106_cbc_m1_36_5_m2_5_0_mtotal_41_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417429", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000210_cbc_m1_93_19_m2_13_31_mtotal_106_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417432", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008016_cbc_m1_5_25_m2_5_25_mtotal_10_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417411", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006132_cbc_m1_106_99_m2_34_51_mtotal_141_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417464", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004494_cbc_m1_19_46_m2_3_04_mtotal_22_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417487", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004101_gn_m1_79_9_m2_21_6_mtotal_101_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417503", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008037_gn_m1_6_48_m2_2_02_mtotal_8_q_3_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417541", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001536_cbc_m1_127_66_m2_14_84_mtotal_142_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417556", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006723_gn_m1_18_91_m2_2_59_mtotal_21_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417577", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008120_cbc_m1_134_98_m2_15_52_mtotal_150_q_8_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417590", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009324_cbc_m1_125_65_m2_22_85_mtotal_148_q_5_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417598", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009360_cbc_m1_62_01_m2_8_49_mtotal_70_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417571", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001685_gn_m1_72_62_m2_51_88_mtotal_124_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417612", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003139_gn_m1_86_32_m2_9_18_mtotal_95_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417607", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001661_gn_m1_100_37_m2_27_13_mtotal_127_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417623", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003734_cbc_m1_26_9_m2_5_6_mtotal_32_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007981_gn_m1_41_25_m2_6_25_mtotal_47_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003106_cbc_m1_62_49_m2_8_01_mtotal_70_q_7_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417674", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009885_gn_m1_71_16_m2_32_34_mtotal_103_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417690", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004721_gn_m1_68_9_m2_10_6_mtotal_79_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417706", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007654_cbc_m1_55_48_m2_7_02_mtotal_62_q_7_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417728", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001632_cbc_m1_19_21_m2_2_29_mtotal_21_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417742", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005389_gn_m1_66_27_m2_55_23_mtotal_121_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417740", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006580_cbc_m1_26_69_m2_2_81_mtotal_29_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417696", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002794_cbc_m1_69_53_m2_23_97_mtotal_93_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417806", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008468_cbc_m1_107_95_m2_38_55_mtotal_146_q_2_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417805", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008818_cbc_m1_60_82_m2_6_68_mtotal_67_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417808", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002323_gn_m1_49_5_m2_5_0_mtotal_54_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417830", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009596_cbc_m1_34_01_m2_5_49_mtotal_39_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417936", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002724_cbc_m1_85_58_m2_8_92_mtotal_94_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417856", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006694_cbc_m1_51_54_m2_6_96_mtotal_58_q_7_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3417971", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005320_cbc_m1_46_27_m2_7_23_mtotal_53_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002995_gn_m1_38_21_m2_27_29_mtotal_65_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418076", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008717_gn_m1_84_91_m2_38_59_mtotal_123_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418086", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002716_cbc_m1_107_83_m2_32_67_mtotal_140_q_3_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418060", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007924_cbc_m1_82_61_m2_15_89_mtotal_98_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418092", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004876_cbc_m1_43_75_m2_4_75_mtotal_48_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418121", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006539_gn_m1_44_19_m2_6_31_mtotal_50_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000214_cbc_m1_93_19_m2_13_31_mtotal_106_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418168", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009330_cbc_m1_83_18_m2_8_32_mtotal_91_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418147", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009695_gn_m1_10_25_m2_10_25_mtotal_20_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418146", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009942_cbc_m1_34_57_m2_3_93_mtotal_38_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418175", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000895_gn_m1_64_79_m2_7_71_mtotal_72_q_8_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418177", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000574_cbc_m1_85_68_m2_13_82_mtotal_99_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418192", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003141_gn_m1_86_32_m2_9_18_mtotal_95_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418186", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000158_cbc_m1_70_57_m2_7_93_mtotal_78_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418190", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008928_cbc_m1_16_38_m2_7_12_mtotal_23_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418189", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001538_cbc_m1_127_66_m2_14_84_mtotal_142_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418179", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005986_cbc_m1_66_81_m2_25_69_mtotal_92_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418208", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001428_cbc_m1_98_33_m2_49_17_mtotal_147_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418191", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005950_cbc_m1_115_93_m2_17_57_mtotal_133_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418195", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005127_gn_m1_99_27_m2_10_23_mtotal_109_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418224", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007764_cbc_m1_19_58_m2_3_92_mtotal_23_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418239", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007862_cbc_m1_42_5_m2_25_0_mtotal_67_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418242", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009103_gn_m1_85_78_m2_10_72_mtotal_96_q_8_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418245", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009261_gn_m1_105_88_m2_24_62_mtotal_130_q_4_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418230", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003908_cbc_m1_103_53_m2_16_97_mtotal_120_q_6_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418288", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009572_cbc_m1_88_83_m2_13_67_mtotal_102_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418314", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002423_gn_m1_98_99_m2_11_51_mtotal_110_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418327", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001126_cbc_m1_74_79_m2_9_71_mtotal_84_q_7_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418400", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001269_gn_m1_57_0_m2_28_5_mtotal_85_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418407", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009397_gn_m1_64_54_m2_8_96_mtotal_73_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418410", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007531_gn_m1_88_65_m2_9_85_mtotal_98_q_9_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418425", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007904_cbc_m1_12_89_m2_4_61_mtotal_17_q_2_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418417", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004156_cbc_m1_130_95_m2_19_55_mtotal_150_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418457", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006577_gn_m1_26_69_m2_2_81_mtotal_29_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418530", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004026_cbc_m1_39_35_m2_23_15_mtotal_62_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418468", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000023_gn_m1_26_76_m2_15_74_mtotal_42_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418532", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006802_cbc_m1_132_37_m2_18_13_mtotal_150_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418490", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005188_cbc_m1_15_82_m2_3_68_mtotal_19_q_4_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418516", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007631_gn_m1_118_36_m2_21_14_mtotal_139_q_5_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418519", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002970_cbc_m1_28_58_m2_2_92_mtotal_31_q_9_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418466", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001253_gn_m1_31_37_m2_7_13_mtotal_38_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418553", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002826_cbc_m1_24_88_m2_2_62_mtotal_27_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418598", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001665_gn_m1_7_42_m2_1_08_mtotal_8_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418581", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004923_gn_m1_34_12_m2_11_38_mtotal_45_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418621", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005373_gn_m1_28_28_m2_4_22_mtotal_32_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418635", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005508_cbc_m1_15_43_m2_9_07_mtotal_24_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418633", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009822_cbc_m1_17_15_m2_2_35_mtotal_19_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418638", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005125_gn_m1_99_27_m2_10_23_mtotal_109_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003286_cbc_m1_66_69_m2_9_81_mtotal_76_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418718", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000879_gn_m1_52_5_m2_35_0_mtotal_87_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418727", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003561_gn_m1_53_94_m2_5_56_mtotal_59_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418732", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003172_cbc_m1_83_76_m2_9_74_mtotal_93_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418708", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001962_cbc_m1_102_08_m2_20_42_mtotal_122_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418761", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001931_gn_m1_95_14_m2_41_36_mtotal_136_q_2_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418811", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007680_cbc_m1_62_33_m2_14_17_mtotal_76_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418760", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000541_gn_m1_24_73_m2_11_77_mtotal_36_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418914", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005917_gn_m1_22_6_m2_2_9_mtotal_25_q_7_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418934", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001190_cbc_m1_90_04_m2_20_46_mtotal_110_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418989", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009166_cbc_m1_69_59_m2_7_91_mtotal_77_q_8_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005305_gn_m1_129_21_m2_19_29_mtotal_148_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3418988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006865_gn_m1_107_11_m2_11_39_mtotal_118_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419039", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002863_gn_m1_31_97_m2_6_53_mtotal_38_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419111", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006134_cbc_m1_106_99_m2_34_51_mtotal_141_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419129", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001305_gn_m1_7_12_m2_2_38_mtotal_9_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419137", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007920_cbc_m1_82_61_m2_15_89_mtotal_98_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419159", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004732_cbc_m1_121_77_m2_18_73_mtotal_140_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419173", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003850_cbc_m1_61_58_m2_8_92_mtotal_70_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419176", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001734_cbc_m1_57_67_m2_5_83_mtotal_63_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419187", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009180_cbc_m1_53_8_m2_11_7_mtotal_65_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419192", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002193_gn_m1_87_95_m2_22_55_mtotal_110_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419193", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006039_gn_m1_91_46_m2_16_04_mtotal_107_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419189", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008569_gn_m1_55_11_m2_42_39_mtotal_97_q_1_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419229", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000873_gn_m1_61_75_m2_23_75_mtotal_85_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419240", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004392_cbc_m1_31_42_m2_12_08_mtotal_43_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419253", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006735_gn_m1_80_94_m2_11_56_mtotal_92_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419256", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009289_gn_m1_6_38_m2_1_12_mtotal_7_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419242", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005882_cbc_m1_69_91_m2_10_59_mtotal_80_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419278", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004915_gn_m1_107_25_m2_41_25_mtotal_148_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419284", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001804_cbc_m1_57_79_m2_7_71_mtotal_65_q_7_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419349", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005473_gn_m1_5_89_m2_0_61_mtotal_6_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419290", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001682_cbc_m1_72_62_m2_51_88_mtotal_124_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419399", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002258_cbc_m1_62_48_m2_13_02_mtotal_75_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419413", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005600_cbc_m1_79_6_m2_10_9_mtotal_90_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419441", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003394_cbc_m1_9_41_m2_1_09_mtotal_10_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419449", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003463_gn_m1_50_17_m2_14_33_mtotal_64_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419514", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008112_cbc_m1_38_18_m2_12_32_mtotal_50_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419502", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000717_gn_m1_125_56_m2_12_94_mtotal_138_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419522", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002742_cbc_m1_93_91_m2_11_59_mtotal_105_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419521", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008867_gn_m1_87_94_m2_12_56_mtotal_100_q_7_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419511", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004928_cbc_m1_44_92_m2_4_58_mtotal_49_q_9_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419530", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006844_cbc_m1_116_02_m2_12_48_mtotal_128_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419551", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009827_gn_m1_17_15_m2_2_35_mtotal_19_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419540", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006926_cbc_m1_22_6_m2_3_9_mtotal_26_q_5_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419601", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000491_gn_m1_97_77_m2_25_73_mtotal_123_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009882_cbc_m1_71_16_m2_32_34_mtotal_103_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419619", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004806_cbc_m1_54_26_m2_10_24_mtotal_64_q_5_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419568", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001281_gn_m1_51_38_m2_32_12_mtotal_83_q_1_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419623", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008853_gn_m1_65_6_m2_6_9_mtotal_72_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419629", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002633_gn_m1_39_51_m2_3_99_mtotal_43_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419690", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004605_gn_m1_92_17_m2_26_33_mtotal_118_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419682", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001884_cbc_m1_86_04_m2_9_46_mtotal_95_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419706", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009291_gn_m1_6_38_m2_1_12_mtotal_7_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419699", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001919_gn_m1_101_58_m2_19_92_mtotal_121_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419713", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005844_cbc_m1_44_06_m2_5_44_mtotal_49_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419708", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002745_gn_m1_93_91_m2_11_59_mtotal_105_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419662", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002568_cbc_m1_126_23_m2_24_27_mtotal_150_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419734", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008992_cbc_m1_124_24_m2_17_26_mtotal_141_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419779", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006751_gn_m1_111_14_m2_17_36_mtotal_128_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419797", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002257_gn_m1_62_48_m2_13_02_mtotal_75_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419834", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002211_gn_m1_48_44_m2_23_06_mtotal_71_q_2_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419812", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004653_gn_m1_26_54_m2_18_96_mtotal_45_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419852", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009508_cbc_m1_94_58_m2_18_92_mtotal_113_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419850", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006546_cbc_m1_23_23_m2_3_27_mtotal_26_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419860", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004824_cbc_m1_27_12_m2_19_38_mtotal_46_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419854", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003631_gn_m1_27_78_m2_7_72_mtotal_35_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419855", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006687_gn_m1_29_75_m2_8_75_mtotal_38_q_3_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419882", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002023_gn_m1_104_56_m2_13_94_mtotal_118_q_7_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419858", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008300_cbc_m1_36_44_m2_5_06_mtotal_41_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419920", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000293_gn_m1_88_3_m2_19_2_mtotal_107_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419968", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006368_cbc_m1_59_32_m2_21_18_mtotal_80_q_2_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420008", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005974_cbc_m1_30_62_m2_21_88_mtotal_52_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420042", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004560_cbc_m1_21_72_m2_12_78_mtotal_34_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420017", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002305_gn_m1_71_15_m2_17_35_mtotal_88_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3419993", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003714_cbc_m1_29_84_m2_4_66_mtotal_34_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420070", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006524_cbc_m1_109_74_m2_12_76_mtotal_122_q_8_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420106", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000567_gn_m1_71_09_m2_7_41_mtotal_78_q_9_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420088", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001865_gn_m1_49_36_m2_41_14_mtotal_90_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420040", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006595_gn_m1_77_3_m2_11_2_mtotal_88_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420134", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004572_cbc_m1_37_2_m2_9_3_mtotal_46_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420082", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003191_gn_m1_21_39_m2_4_11_mtotal_25_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420141", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002521_gn_m1_27_08_m2_5_42_mtotal_32_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420252", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008643_gn_m1_25_88_m2_13_62_mtotal_39_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420280", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008177_gn_m1_77_48_m2_18_02_mtotal_95_q_4_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420285", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000767_gn_m1_50_16_m2_5_34_mtotal_55_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420247", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001157_gn_m1_14_47_m2_6_03_mtotal_20_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420275", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002284_cbc_m1_108_4_m2_27_1_mtotal_135_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420271", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000815_gn_m1_69_31_m2_10_19_mtotal_79_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420238", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001121_gn_m1_59_11_m2_17_39_mtotal_76_q_3_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420364", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009529_gn_m1_97_67_m2_48_83_mtotal_146_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009643_gn_m1_52_18_m2_19_32_mtotal_71_q_2_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420304", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007000_cbc_m1_76_75_m2_7_75_mtotal_84_q_9_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420375", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003298_cbc_m1_37_59_m2_28_91_mtotal_66_q_1_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420328", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002615_gn_m1_115_27_m2_16_23_mtotal_131_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420411", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009003_gn_m1_47_79_m2_7_71_mtotal_55_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420435", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004127_gn_m1_65_36_m2_26_14_mtotal_91_q_2_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420468", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009555_gn_m1_104_93_m2_20_57_mtotal_125_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420477", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005756_cbc_m1_71_23_m2_7_27_mtotal_78_q_9_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420511", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009564_cbc_m1_34_24_m2_9_26_mtotal_43_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420525", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004401_gn_m1_71_88_m2_15_63_mtotal_87_q_4_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420572", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001706_cbc_m1_91_91_m2_14_59_mtotal_106_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420578", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008269_gn_m1_96_96_m2_10_54_mtotal_107_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420576", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000492_cbc_m1_124_16_m2_24_34_mtotal_148_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420563", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001900_cbc_m1_71_75_m2_71_75_mtotal_143_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420588", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006604_cbc_m1_78_72_m2_10_78_mtotal_89_q_7_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420591", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009336_cbc_m1_35_7_m2_23_8_mtotal_59_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420595", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004178_cbc_m1_46_28_m2_27_22_mtotal_73_q_1_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420619", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003836_cbc_m1_61_35_m2_21_15_mtotal_82_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420654", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002734_cbc_m1_18_38_m2_6_12_mtotal_24_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420633", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002519_gn_m1_13_7_m2_2_8_mtotal_16_q_4_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005171_gn_m1_57_44_m2_13_06_mtotal_70_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420698", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002990_cbc_m1_123_28_m2_15_22_mtotal_138_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420663", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009334_cbc_m1_83_18_m2_8_32_mtotal_91_q_10_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420773", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002627_gn_m1_46_12_m2_15_38_mtotal_61_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420754", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002657_gn_m1_14_41_m2_2_09_mtotal_16_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420737", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002042_cbc_m1_61_85_m2_6_65_mtotal_68_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420819", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007967_gn_m1_91_78_m2_41_72_mtotal_133_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420715", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005267_gn_m1_27_5_m2_5_0_mtotal_32_q_5_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420826", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004889_gn_m1_68_0_m2_8_5_mtotal_76_q_8_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420840", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003022_cbc_m1_39_19_m2_10_31_mtotal_49_q_3_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3420936", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004905_gn_m1_21_76_m2_16_74_mtotal_38_q_1_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421005", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000598_cbc_m1_78_8_m2_19_7_mtotal_98_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421030", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009317_gn_m1_46_5_m2_31_0_mtotal_77_q_1_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421057", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006049_gn_m1_5_67_m2_1_83_mtotal_7_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421042", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004052_cbc_m1_96_11_m2_53_39_mtotal_149_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421055", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008428_cbc_m1_70_92_m2_10_58_mtotal_81_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421058", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004622_cbc_m1_8_54_m2_0_96_mtotal_9_q_8_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421069", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001482_cbc_m1_112_62_m2_17_88_mtotal_130_q_6_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421082", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009567_gn_m1_34_24_m2_9_26_mtotal_43_q_3_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421094", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005730_cbc_m1_14_88_m2_1_62_mtotal_16_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421137", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002400_cbc_m1_45_5_m2_7_0_mtotal_52_q_6_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006943_gn_m1_24_66_m2_2_84_mtotal_27_q_8_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421206", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008764_cbc_m1_19_71_m2_6_79_mtotal_26_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421195", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009658_cbc_m1_67_98_m2_11_52_mtotal_79_q_5_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421178", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004526_cbc_m1_21_66_m2_9_84_mtotal_31_q_2_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421218", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001985_gn_m1_118_04_m2_21_46_mtotal_139_q_5_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421241", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008765_gn_m1_19_71_m2_6_79_mtotal_26_q_2_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421232", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007951_gn_m1_37_86_m2_6_64_mtotal_44_q_5_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421252", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007462_cbc_m1_60_81_m2_11_69_mtotal_72_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421257", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002771_gn_m1_20_79_m2_6_71_mtotal_27_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421287", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004174_cbc_m1_74_89_m2_41_61_mtotal_116_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421303", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002036_cbc_m1_68_31_m2_10_19_mtotal_78_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421323", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007356_cbc_m1_75_54_m2_53_96_mtotal_129_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421346", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004833_gn_m1_83_61_m2_23_89_mtotal_107_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421347", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002598_cbc_m1_107_96_m2_24_54_mtotal_132_q_4_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421345", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004039_gn_m1_56_12_m2_8_38_mtotal_64_q_6_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421364", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005697_gn_m1_15_61_m2_2_89_mtotal_18_q_5_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421377", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005793_gn_m1_79_92_m2_11_58_mtotal_91_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421446", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003312_cbc_m1_41_71_m2_29_79_mtotal_71_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421431", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004354_cbc_m1_23_32_m2_5_18_mtotal_28_q_4_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421477", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004095_gn_m1_44_17_m2_11_33_mtotal_55_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421521", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001591_gn_m1_50_38_m2_8_12_mtotal_58_q_6_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421574", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001724_cbc_m1_30_42_m2_6_08_mtotal_36_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421580", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009396_cbc_m1_64_54_m2_8_96_mtotal_73_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421522", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004640_cbc_m1_94_82_m2_52_68_mtotal_147_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421607", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004980_cbc_m1_44_68_m2_24_82_mtotal_69_q_1_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421682", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000224_cbc_m1_83_52_m2_8_98_mtotal_92_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421640", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008735_gn_m1_58_29_m2_8_21_mtotal_66_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421705", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006890_cbc_m1_6_59_m2_0_91_mtotal_7_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421729", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009281_gn_m1_55_12_m2_39_38_mtotal_94_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421765", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003212_cbc_m1_50_54_m2_12_96_mtotal_63_q_3_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421792", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008934_cbc_m1_107_5_m2_25_0_mtotal_132_q_4_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421797", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006782_cbc_m1_67_71_m2_8_79_mtotal_76_q_7_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421837", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006326_cbc_m1_131_92_m2_18_58_mtotal_150_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421872", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007022_cbc_m1_13_75_m2_13_75_mtotal_27_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421893", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008653_gn_m1_116_4_m2_29_1_mtotal_145_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421961", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004424_cbc_m1_5_52_m2_0_98_mtotal_6_q_5_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421958", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009744_cbc_m1_80_22_m2_10_28_mtotal_90_q_7_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421927", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008284_cbc_m1_87_18_m2_36_32_mtotal_123_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3421974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002764_cbc_m1_82_92_m2_16_58_mtotal_99_q_5_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422025", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004764_cbc_m1_13_61_m2_3_89_mtotal_17_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422048", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001326_cbc_m1_82_51_m2_13_99_mtotal_96_q_5_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422130", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000871_gn_m1_61_75_m2_23_75_mtotal_85_q_2_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422109", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008555_gn_m1_15_47_m2_3_03_mtotal_18_q_5_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422165", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009405_gn_m1_50_29_m2_6_21_mtotal_56_q_8_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422180", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000501_gn_m1_94_12_m2_12_38_mtotal_106_q_7_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422161", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002535_gn_m1_71_94_m2_20_56_mtotal_92_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422233", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009386_cbc_m1_87_22_m2_12_28_mtotal_99_q_7_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422256", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009049_gn_m1_109_86_m2_16_64_mtotal_126_q_6_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422255", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005935_gn_m1_74_71_m2_17_79_mtotal_92_q_4_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422271", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003335_gn_m1_49_06_m2_9_44_mtotal_58_q_5_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422339", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003303_gn_m1_9_0_m2_4_5_mtotal_13_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422369", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005470_cbc_m1_4_91_m2_2_59_mtotal_7_q_1_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422371", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003462_cbc_m1_50_17_m2_14_33_mtotal_64_q_3_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422377", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003036_cbc_m1_96_06_m2_10_44_mtotal_106_q_9_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422430", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000334_cbc_m1_119_64_m2_12_86_mtotal_132_q_9_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422391", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004546_cbc_m1_85_0_m2_42_5_mtotal_127_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422392", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007444_cbc_m1_31_88_m2_10_62_mtotal_42_q_3_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422437", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002649_gn_m1_64_82_m2_6_68_mtotal_71_q_9_7.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422454", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001136_cbc_m1_85_66_m2_17_84_mtotal_103_q_4_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422487", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000078_cbc_m1_55_96_m2_15_54_mtotal_71_q_3_6.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422581", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0002364_cbc_m1_65_25_m2_65_25_mtotal_130_q_1_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422622", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007015_gn_m1_60_97_m2_9_53_mtotal_70_q_6_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422644", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006679_gn_m1_111_07_m2_15_43_mtotal_126_q_7_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422638", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007495_gn_m1_55_6_m2_13_9_mtotal_69_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422693", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004335_gn_m1_52_95_m2_16_55_mtotal_69_q_3_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422753", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0009028_cbc_m1_96_43_m2_16_07_mtotal_112_q_6_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422657", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007869_gn_m1_12_46_m2_3_04_mtotal_15_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422761", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007396_cbc_m1_58_11_m2_6_39_mtotal_64_q_9_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422771", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001236_cbc_m1_42_07_m2_4_43_mtotal_46_q_9_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422750", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004281_gn_m1_39_6_m2_9_9_mtotal_49_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422788", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005606_cbc_m1_85_67_m2_42_83_mtotal_128_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422756", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000639_gn_m1_72_92_m2_8_58_mtotal_81_q_8_5.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422820", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007901_gn_m1_96_4_m2_24_1_mtotal_120_q_4_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422863", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003849_gn_m1_61_58_m2_8_92_mtotal_70_q_6_9.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422872", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0008264_cbc_m1_22_09_m2_18_41_mtotal_40_q_1_2.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0007540_cbc_m1_67_34_m2_7_16_mtotal_74_q_9_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422916", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0001277_gn_m1_21_0_m2_10_5_mtotal_31_q_2_0.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422934", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0004291_gn_m1_94_24_m2_39_26_mtotal_133_q_2_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006265_gn_m1_46_38_m2_33_12_mtotal_79_q_1_4.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422952", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0000317_gn_m1_109_41_m2_16_09_mtotal_125_q_6_8.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422870", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005978_cbc_m1_120_19_m2_29_31_mtotal_149_q_4_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422999", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0005721_gn_m1_92_96_m2_17_54_mtotal_110_q_5_3.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: normal", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["normal"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "GWOSC", "dataset_name": "GWOSC_GW_Event", "id": "GW_3422983", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0006762_cbc_m1_95_65_m2_30_85_mtotal_126_q_3_1.csv", "length": 64000, "timestamps": [], "fs": 16000}, "input_text": "Please analyze the provided time-series data and determine whether a gravitational wave event has occurred. Classify the result as 'normal' (no gravitational wave event detected) or 'anomaly' (a gravitational wave event is detected).", "gt_text": "The classification result is: anomaly", "gt_result": {"class_list": ["normal", "anomaly"], "gt_class": ["anomaly"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Gravitational Waves"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_29991", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5351171982585894144.csv", "length": 49, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_29995", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220740.76+510829.9.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30023", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J041121.05+232307.9.csv", "length": 244, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30057", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53825177.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30052", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10155603.csv", "length": 196, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30049", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J234224.73+510902.1.csv", "length": 298, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30097", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10337599.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30129", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35309866.csv", "length": 335, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30143", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J215859.66+803908.0.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30114", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J100801.83-382802.1.csv", "length": 753, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30138", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32990942.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30156", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220343.31+145103.3.csv", "length": 59, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30162", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59494552.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30166", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J182220.83+184103.1.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30184", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J183504.08+375256.8.csv", "length": 530, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30188", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32984207.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30200", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5369485929273451520.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30203", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP44512765.csv", "length": 202, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30210", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31677198.csv", "length": 149, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30218", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J212952.70-011018.4.csv", "length": 246, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30250", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18897382.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30284", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP27756175.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30291", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51869525.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30297", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17666137.csv", "length": 230, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30308", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220346.45+202536.8.csv", "length": 64, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30322", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190326.20+404904.4.csv", "length": 81, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30332", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ164151.76+552347.5.csv", "length": 227, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30328", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28618601.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30347", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190657.05+001715.7.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30343", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29499387.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30348", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192548.90+384443.5.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30324", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J125940.64-435637.1.csv", "length": 1088, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30389", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5304897072906526464.csv", "length": 59, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30382", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6725526088089709312.csv", "length": 37, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30401", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225237.01+643230.1.csv", "length": 161, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30396", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29727544.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30412", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191018.23+012707.3.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30402", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194338.87+511043.4.csv", "length": 290, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30405", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25396674.csv", "length": 315, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30415", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10103360.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30421", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51859200.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30466", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP11076764.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30520", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ175557.63+433800.7.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30548", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ060206.75+081724.5.csv", "length": 86, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30550", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35038475.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30546", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32972676.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30562", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J062403.24-342502.3.csv", "length": 300, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30578", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10478281.csv", "length": 124, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30592", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191442.20+060024.4.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30600", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30031940.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30604", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30938369.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30602", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP575328.csv", "length": 244, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30621", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201134.44+371013.5.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30637", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21642154.csv", "length": 266, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30624", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190240.18+215238.3.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30638", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP827900.csv", "length": 112, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30663", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223400.86+220543.1.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30660", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29012634.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30676", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID3503715955074017920.csv", "length": 53, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30669", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29390096.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30666", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29816381.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30689", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195242.59-054718.5.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30707", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29437475.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30723", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193834.87+141856.1.csv", "length": 130, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30727", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32799613.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30733", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192633.35+482533.1.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30735", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46578450.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30760", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP60040441.csv", "length": 219, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30774", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28750530.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30785", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51895686.csv", "length": 299, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30818", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200509.43+415657.6.csv", "length": 177, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30831", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192904.43+211243.5.csv", "length": 309, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30845", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ153734.67+153503.4.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30837", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31144289.csv", "length": 200, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30841", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29473229.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30857", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055053.58+303945.4.csv", "length": 105, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30852", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46709079.csv", "length": 261, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5509635586917643264.csv", "length": 40, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30871", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13788159.csv", "length": 292, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30886", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59380890.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30865", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51609770.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30873", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35139854.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30905", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ170746.31+752421.2.csv", "length": 112, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30904", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193458.97+163150.9.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30913", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56398344.csv", "length": 232, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30923", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32694621.csv", "length": 432, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30929", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182704.96+505442.9.csv", "length": 265, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30948", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35178185.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30950", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31036290.csv", "length": 197, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30968", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ121535.31+323538.0.csv", "length": 99, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192430.34-000557.0.csv", "length": 50, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_30994", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10074949.csv", "length": 196, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31006", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185948.41+294939.0.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP20173689.csv", "length": 308, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31042", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34714519.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31055", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ122635.70+082306.1.csv", "length": 140, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31065", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ033621.54+604145.3.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31074", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51731066.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31072", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51899770.csv", "length": 140, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31089", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24889508.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31091", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ160837.45+550603.8.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31104", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51821805.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31117", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10194947.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31127", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201137.74+532354.2.csv", "length": 78, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31142", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230012.73+561650.3.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31131", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51652173.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31138", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30932417.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31168", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ183641.54+172347.6.csv", "length": 67, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31159", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215604.62+483824.8.csv", "length": 138, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31152", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9627586.csv", "length": 527, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31184", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10410906.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31177", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203420.61+340540.9.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31185", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58787741.csv", "length": 473, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31193", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP42614647.csv", "length": 286, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31222", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5922366550901970304.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31234", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190241.34+214305.4.csv", "length": 135, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31219", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59344850.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31266", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4658656949042309504.csv", "length": 40, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31261", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ062926.11+212311.2.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31297", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35092564.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31292", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221228.97+610649.4.csv", "length": 335, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31317", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202017.30-120338.8.csv", "length": 67, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31335", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J202845.07+050818.4.csv", "length": 269, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31336", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29731283.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31345", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29487267.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31354", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ062713.04+030659.9.csv", "length": 83, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31353", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30046178.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31364", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10409784.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31365", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J023414.27+421427.9.csv", "length": 319, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31381", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID1886146952603466624.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31390", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53915244.csv", "length": 139, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31389", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32615659.csv", "length": 75, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31395", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10388701.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31405", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J212631.06+355934.3.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31424", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200520.52+320415.1.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31423", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015313.27+564121.8.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31426", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5358762358011232896.csv", "length": 62, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31437", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56860722.csv", "length": 235, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31452", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223553.70+393827.6.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31457", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32832943.csv", "length": 253, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31475", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28862115.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31496", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214719.96+512632.1.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31500", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015355.14+585951.4.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31515", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ002905.42+513552.9.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31533", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J223043.62+334424.9.csv", "length": 232, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31531", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10124474.csv", "length": 185, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31536", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53978791.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31543", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193918.25+103927.3.csv", "length": 135, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31566", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13783373.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31565", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185157.99+364903.0.csv", "length": 277, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31586", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32931775.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31579", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015415.28+633117.1.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31592", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J190719.99-122722.1.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31584", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59334024.csv", "length": 249, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31603", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31706174.csv", "length": 142, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31596", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46021080.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31611", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015256.99+322852.4.csv", "length": 90, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39850546.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31643", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID1975051538689813888.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31638", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ164706.89+182344.9.csv", "length": 67, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31625", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202556.87+331200.7.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31647", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31080406.csv", "length": 88, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31649", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222616.84+611710.3.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31659", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5411388145606855296.csv", "length": 62, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31671", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59994946.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31661", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191314.90+135801.2.csv", "length": 290, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31660", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195050.98+302549.4.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31674", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29533331.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31682", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46581392.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31703", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29982312.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31710", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230854.66+575355.0.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31709", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ011228.95+681504.4.csv", "length": 199, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31719", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51802456.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31721", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4316998661514305152.csv", "length": 41, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31728", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5890290949210440320.csv", "length": 50, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31731", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30203255.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31735", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32979749.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31739", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13943780.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31750", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J070017.43-174556.9.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31753", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192552.53+021916.5.csv", "length": 158, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31759", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193139.77+080735.9.csv", "length": 172, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31762", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59508988.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31773", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56824274.csv", "length": 260, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31793", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35377682.csv", "length": 204, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31791", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28890692.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31820", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13703696.csv", "length": 343, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31844", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10430678.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31836", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ160919.76+434423.7.csv", "length": 271, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31859", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ031533.73+602308.3.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31867", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51848919.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31895", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194250.13+581917.4.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31908", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ175905.64+342119.0.csv", "length": 248, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31914", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25307671.csv", "length": 139, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31930", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35261395.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31941", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201853.29+390559.3.csv", "length": 153, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31963", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225522.87+591315.4.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31962", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35203347.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31971", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32922192.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31967", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224322.08+571226.9.csv", "length": 306, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31994", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32921157.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_31991", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J183234.45-652253.9.csv", "length": 527, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32005", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J203914.51-225525.8.csv", "length": 426, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32011", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35322300.csv", "length": 335, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32032", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29893434.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32039", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29883357.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29001763.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32065", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32870996.csv", "length": 77, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32074", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ012939.71+602400.8.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32053", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19141376.csv", "length": 260, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32091", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29852195.csv", "length": 246, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32093", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59422551.csv", "length": 313, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32135", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10533008.csv", "length": 312, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32139", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J185417.57+331128.1.csv", "length": 347, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32146", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J205417.62+554904.0.csv", "length": 158, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32150", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ001033.49+584250.9.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32151", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10377354.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32154", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29601703.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32180", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28760062.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32196", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180421.04+440020.8.csv", "length": 258, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32201", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28990473.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32206", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191009.64+025042.5.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32204", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195310.07+215305.8.csv", "length": 155, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32215", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ231038.19+575957.8.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32223", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192605.48+253319.1.csv", "length": 139, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32212", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J172601.57+032240.6.csv", "length": 440, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32250", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200348.65+371535.3.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32275", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32945042.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32295", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34620292.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32311", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29533610.csv", "length": 54, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32326", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215359.27+593012.1.csv", "length": 148, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32331", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193447.54+481047.6.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32344", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID509360890554910336.csv", "length": 114, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32352", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ112921.60+281619.6.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32365", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34705704.csv", "length": 150, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32348", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51634184.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32368", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17265605.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32395", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46512201.csv", "length": 265, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32410", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29857848.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32404", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J030925.22+233449.0.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32430", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP14106516.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32461", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055502.51+192557.7.csv", "length": 95, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32446", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J113645.09-043120.9.csv", "length": 420, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32469", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53279574.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32482", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202653.23+494315.5.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32475", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59377718.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32505", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185557.13-030839.5.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32510", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J041943.98+011559.2.csv", "length": 480, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32531", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205736.06+382406.9.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32535", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46756572.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32540", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ042404.21+451544.4.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32530", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51847171.csv", "length": 401, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32551", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190015.26+060715.3.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32547", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ024027.69+520814.3.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32570", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35190572.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32561", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230214.00+592333.5.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32566", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10465072.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32556", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35042758.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32571", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9820720.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32586", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ175712.14+115838.3.csv", "length": 57, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32583", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9539390.csv", "length": 203, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32601", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203612.28+354131.9.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32602", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53809393.csv", "length": 350, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32617", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP44540687.csv", "length": 202, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32612", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222501.59+633252.5.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32613", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ020855.84+554002.7.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055242.57+171114.8.csv", "length": 94, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32624", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32944737.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32639", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35343437.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32643", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J123415.25+481307.1.csv", "length": 148, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32627", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP20174648.csv", "length": 317, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32649", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP49767256.csv", "length": 199, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32663", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ035435.02+354223.4.csv", "length": 117, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32674", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46045212.csv", "length": 297, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32677", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ233256.90+554843.8.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32687", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9534533.csv", "length": 410, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32699", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192544.45+211955.1.csv", "length": 115, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32700", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ231353.50+542251.7.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32731", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19106924.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32782", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10992050.csv", "length": 99, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32759", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29989350.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32796", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195347.73+171117.2.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32798", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ233345.72+435049.6.csv", "length": 57, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32790", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214916.90+494356.3.csv", "length": 318, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32797", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32907911.csv", "length": 183, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32804", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2995772850405288576.csv", "length": 43, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32818", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194513.42+175828.1.csv", "length": 105, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32833", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10174553.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32843", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ023031.97+605343.4.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32865", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34990174.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32875", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28964536.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32904", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35019426.csv", "length": 95, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32897", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51715274.csv", "length": 268, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32898", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19498951.csv", "length": 300, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32899", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195936.84+365219.3.csv", "length": 428, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32922", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ050824.48+300923.2.csv", "length": 114, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32934", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51887340.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32932", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59373781.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32959", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ001740.40+613603.4.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32968", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214208.32+574048.3.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32965", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24607803.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32964", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J135533.61+170747.2.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32695936.csv", "length": 185, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_32995", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ174935.23+521802.4.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33024", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39145088.csv", "length": 190, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33045", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24811385.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33038", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29543954.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33036", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP48333565.csv", "length": 180, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33046", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5309561789656283136.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33052", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35054215.csv", "length": 399, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33056", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29892882.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33075", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28936402.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33073", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ014733.61+611329.0.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33084", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35034755.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33085", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191600.57+505940.5.csv", "length": 185, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33081", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46042927.csv", "length": 281, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13620721.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33152", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223637.99+543144.7.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33166", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194000.06+313238.1.csv", "length": 128, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35230672.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33179", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221307.48+614923.6.csv", "length": 335, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33199", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ002841.23+451600.2.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33202", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J184042.54-335053.6.csv", "length": 274, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33209", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29979041.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33220", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP49488464.csv", "length": 340, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33229", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56615209.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33246", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32803825.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33240", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J012720.60-300643.2.csv", "length": 294, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33267", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191854.01+014536.1.csv", "length": 95, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33266", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ010213.31+391701.3.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33276", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10798487.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33285", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29840529.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33300", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25242710.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33289", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17313879.csv", "length": 252, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33316", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19919370.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33307", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230510.55+631329.8.csv", "length": 315, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58939787.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33325", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J113433.30-702832.1.csv", "length": 370, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33288", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J080125.70-630010.3.csv", "length": 1318, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33337", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10488202.csv", "length": 192, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33335", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J052434.80+685338.5.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33338", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29910844.csv", "length": 289, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33347", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195023.89+210706.6.csv", "length": 147, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33356", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP823966.csv", "length": 113, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33357", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J025730.45-174352.4.csv", "length": 278, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33363", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ031118.20+491920.9.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33362", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J191014.74-541325.8.csv", "length": 463, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33367", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195944.19+435606.6.csv", "length": 261, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33376", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13948813.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33393", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194646.62+301007.6.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33409", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5031669983878164992.csv", "length": 39, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190616.20+162739.8.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33425", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29965419.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33452", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10450569.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33443", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51808514.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33474", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ070710.84-041657.3.csv", "length": 74, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33465", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32676735.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33495", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201748.35+404845.4.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33498", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46367294.csv", "length": 497, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33532", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32658439.csv", "length": 77, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33541", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5722584188622683136.csv", "length": 46, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33524", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J081255.69-802625.9.csv", "length": 589, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33523", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J061601.78-592336.8.csv", "length": 750, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33568", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ183955.67-040114.0.csv", "length": 97, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33561", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ011900.00+573244.4.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33562", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29916953.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33559", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221751.54+512606.1.csv", "length": 296, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33555", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58970962.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33606", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2199988356188744192.csv", "length": 42, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33595", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182500.64+563906.1.csv", "length": 245, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33626", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35401106.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33640", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ035225.18+684931.5.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33719", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ172406.10+341430.8.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33700", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220403.46+465701.1.csv", "length": 158, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33707", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2964805036690596736.csv", "length": 88, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33706", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59586635.csv", "length": 225, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33728", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ005704.25+624846.6.csv", "length": 267, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33733", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ231003.82+621355.0.csv", "length": 318, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33738", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35023462.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33741", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ042435.78+465442.0.csv", "length": 120, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33753", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215406.71+485930.1.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33768", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194129.42+103025.2.csv", "length": 194, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33765", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29827526.csv", "length": 390, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33780", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202728.81+521319.3.csv", "length": 76, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33771", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29600121.csv", "length": 286, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33775", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32704499.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33789", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201437.26+224015.8.csv", "length": 199, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33785", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP27869192.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33828", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35337022.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33835", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182137.34+333747.3.csv", "length": 237, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33823", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J122423.41-475409.9.csv", "length": 986, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33833", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J035012.43+243157.8.csv", "length": 480, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33868", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ031536.75+470158.1.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33860", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18922941.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33853", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19109858.csv", "length": 261, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33878", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221206.31+523158.0.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33894", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ184555.26+135924.8.csv", "length": 206, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33907", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J154658.36-392241.5.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33908", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221517.78+523803.8.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33923", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J045632.37-665419.0.csv", "length": 705, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33952", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ003632.13+693632.2.csv", "length": 132, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_33945", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ235847.98+664807.9.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29014177.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34029", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56504336.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34053", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59502011.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34058", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230532.47+632057.7.csv", "length": 313, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34095", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210024.63+385357.8.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34099", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51643508.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34097", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ025518.72+512510.6.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34109", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP923427.csv", "length": 182, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34115", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222714.26+443216.6.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34138", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195628.41+232832.0.csv", "length": 150, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194439.92+353805.8.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34142", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32703099.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34145", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13909677.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34176", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID3054374999104019328.csv", "length": 50, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34190", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ000621.45+593232.5.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34196", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J094302.80-170716.0.csv", "length": 544, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34210", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29989035.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34241", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10996646.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34242", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ054950.63+153810.2.csv", "length": 94, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34283", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213631.02+450506.5.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34273", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP45879426.csv", "length": 249, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34286", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5834615666118073984.csv", "length": 64, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34291", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21267615.csv", "length": 254, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34308", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51949786.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35320191.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34324", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29934506.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34321", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19771350.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34316", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34950121.csv", "length": 275, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34332", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193728.57+090640.3.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34342", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28965561.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34343", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200548.92+303517.1.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34338", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56517288.csv", "length": 117, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34348", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J201703.44-371423.4.csv", "length": 192, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34359", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194135.77+162814.0.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34362", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10565677.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34381", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29480695.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34368", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ142813.33+514239.0.csv", "length": 249, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34415", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ071508.05+005642.6.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34416", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195001.63+193221.5.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34419", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180032.88+400832.2.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34446", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ030508.48+470227.3.csv", "length": 56, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32880174.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34435", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP334785.csv", "length": 247, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34474", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35347538.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34482", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J141826.59+001428.6.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34487", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205158.49+443724.2.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34517", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28982007.csv", "length": 277, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34528", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10518747.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34527", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203423.30+082046.3.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34542", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP2072512.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34552", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225303.57+582906.2.csv", "length": 318, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34566", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46739784.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34570", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211533.46+642412.8.csv", "length": 231, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34600", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193536.64+582503.5.csv", "length": 223, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35272611.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34641", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J173523.36-523331.5.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34644", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ024500.98+561613.2.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34647", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28719614.csv", "length": 189, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34667", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6067498447929365760.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34661", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222818.47+562531.9.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34679", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32692369.csv", "length": 77, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34682", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28550039.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34680", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21252818.csv", "length": 266, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34696", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30258051.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34706", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP40663128.csv", "length": 177, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34713", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201353.53+344842.0.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34714", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32990861.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34725", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190536.36+205854.5.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34724", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194935.03+423647.4.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34768", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29353656.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34787", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ153914.08-123316.9.csv", "length": 78, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34786", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ212503.73+213011.1.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34798", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J113739.98-374218.9.csv", "length": 259, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34820", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192510.64+022411.8.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34842", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ232400.13+742505.1.csv", "length": 57, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29975381.csv", "length": 169, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34845", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34968400.csv", "length": 272, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34859", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP6130656.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34856", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34942749.csv", "length": 275, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ020313.84+450353.7.csv", "length": 59, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34896", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5752461222379143552.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34894", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59214915.csv", "length": 232, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34903", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39215828.csv", "length": 379, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34954", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222810.74+522530.9.csv", "length": 135, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34955", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10165261.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34957", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191429.09+694904.2.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34975", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J035439.26+343519.2.csv", "length": 137, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34988", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29468545.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_34997", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ070513.85-122106.6.csv", "length": 66, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35000", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46837165.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35031", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191650.11+235121.9.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35060", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10370207.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35054", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32865775.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35059", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59463872.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35087", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4929368054776557184.csv", "length": 69, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35102", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29984053.csv", "length": 176, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35114", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192547.33+090137.8.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35115", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ070345.47+353154.6.csv", "length": 100, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35113", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35188105.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35140", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203452.74+503759.4.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35151", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31114645.csv", "length": 131, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222452.31+533319.2.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35176", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ234842.33+535122.8.csv", "length": 149, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35174", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ030936.14+531656.0.csv", "length": 120, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35195", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ171507.41+153201.1.csv", "length": 69, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35209", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4490848595322325120.csv", "length": 41, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35206", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46832475.csv", "length": 497, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35213", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185915.45+054816.0.csv", "length": 255, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35228", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190030.27-060806.9.csv", "length": 120, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35224", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ051346.56+433526.1.csv", "length": 113, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35245", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193849.86+264101.9.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35223", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30262883.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35253", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP37841262.csv", "length": 305, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35260", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32702189.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35268", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ183054.10+080213.8.csv", "length": 182, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35269", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51736956.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35275", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35256519.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35281", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191920.16+301222.2.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35297", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055946.59+271852.0.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35299", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191122.96+361741.4.csv", "length": 81, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35294", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6071430743915130752.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35309", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10381847.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35314", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32686015.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J065015.37-053417.7.csv", "length": 189, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35321", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203614.90+462523.4.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35343", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203839.73+263040.8.csv", "length": 71, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35357", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28672336.csv", "length": 93, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35350", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185633.77+323058.3.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35370", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ012153.36+320906.5.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35390", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59519509.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35403", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055700.72+125943.0.csv", "length": 137, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35400", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J170039.10-263658.2.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35406", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29005633.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35408", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19098045.csv", "length": 247, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203729.37+463511.0.csv", "length": 130, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35463", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ014956.38+535418.9.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35459", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210442.85+572129.6.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35474", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25555717.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35494", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4440521439859208320.csv", "length": 71, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35479", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ202244.69+385150.2.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35485", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ024517.82+390700.4.csv", "length": 103, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35490", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28970867.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35487", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51909608.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35497", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34617705.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35505", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ183205.68+153229.7.csv", "length": 128, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35498", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32922128.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35519", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28538214.csv", "length": 204, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35531", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4084046404209313920.csv", "length": 24, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35557", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51868349.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35558", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J161550.86+373221.3.csv", "length": 273, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35580", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59292293.csv", "length": 478, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35598", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19727572.csv", "length": 286, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35599", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32907781.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35595", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ232551.09+562813.1.csv", "length": 306, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30935196.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35636", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193325.93+310121.3.csv", "length": 83, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53360939.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35630", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J152807.34+060802.1.csv", "length": 228, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35656", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ212942.93+452632.8.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35671", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29964178.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35664", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203400.83+572009.1.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35674", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP50791569.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35675", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18827098.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35686", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13641251.csv", "length": 94, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35693", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220012.18+492204.0.csv", "length": 160, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35708", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211537.75+100238.2.csv", "length": 72, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35714", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29816384.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35729", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17077698.csv", "length": 264, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35734", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35143581.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35732", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10969048.csv", "length": 99, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35759", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221230.03+464703.5.csv", "length": 89, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35765", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30609689.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35761", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J143907.80-001435.0.csv", "length": 434, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35783", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193222.20+174632.1.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35789", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ234024.47+562139.8.csv", "length": 146, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35794", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210530.38+783756.0.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35781", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224219.56+583433.4.csv", "length": 333, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35800", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213114.57+463519.8.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35810", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195444.39+362821.6.csv", "length": 169, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35824", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195201.33+333337.1.csv", "length": 135, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35804", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28485563.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51803472.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35832", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215717.77+542717.3.csv", "length": 336, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35835", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10386252.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35838", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30056955.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35854", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6035314299496494720.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35848", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J032405.68-084131.4.csv", "length": 268, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35874", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30138793.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35872", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ170225.27+232141.9.csv", "length": 226, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35894", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190522.03+471207.4.csv", "length": 236, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35886", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31370099.csv", "length": 246, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35898", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185912.19-001211.9.csv", "length": 92, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35915", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP50768400.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35905", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195924.27+282602.4.csv", "length": 155, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35903", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59229630.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35911", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32858510.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35921", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35262660.csv", "length": 140, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35914", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J134407.48-001003.8.csv", "length": 226, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35926", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6709671233497390336.csv", "length": 35, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35940", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4795804707993358080.csv", "length": 43, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35929", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213128.60+491558.9.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35930", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190516.25+294130.7.csv", "length": 343, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35946", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59399382.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35964", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID1191345695083956608.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35961", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29721778.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35966", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31016146.csv", "length": 222, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35960", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59180964.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35974", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222732.88+561236.5.csv", "length": 137, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35989", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211055.29+162029.1.csv", "length": 80, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36014", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190841.13+245524.0.csv", "length": 72, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36020", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ072807.60-124028.4.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_35999", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J183421.29+295216.3.csv", "length": 503, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36022", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224244.91+531223.2.csv", "length": 243, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36033", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223130.31+514244.6.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36031", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200427.53+360832.9.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36044", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ064152.38-050222.0.csv", "length": 79, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36047", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201025.08+333627.2.csv", "length": 347, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36077", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055131.81+354607.1.csv", "length": 105, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36090", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191233.57+462533.9.csv", "length": 410, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36106", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID1972891410655243904.csv", "length": 62, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36105", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ054903.73+523458.0.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36119", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25291349.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36112", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191913.47+440654.1.csv", "length": 267, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36134", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28719843.csv", "length": 190, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36151", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID946933709156195456.csv", "length": 30, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36141", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31401797.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36146", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J081540.30-622107.6.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36164", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59320173.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36167", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10844686.csv", "length": 216, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36172", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP33156911.csv", "length": 205, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36175", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59286646.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36171", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP641466.csv", "length": 253, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36186", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51773591.csv", "length": 115, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36217", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID209012109560991360.csv", "length": 41, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36196", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190846.47+191754.4.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36229", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214248.61+480003.0.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36224", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ234353.90+513141.7.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36245", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30847096.csv", "length": 215, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36259", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200431.82+151933.3.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36262", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J225535.01-172718.2.csv", "length": 189, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36264", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30868182.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36238", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29610554.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36291", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51844721.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36287", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19015423.csv", "length": 284, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36306", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210918.30+174909.4.csv", "length": 80, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36315", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25593066.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36321", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ000018.70+612543.2.csv", "length": 252, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36319", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35050806.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36331", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ155716.06+695116.6.csv", "length": 351, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36350", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J044912.48-690940.4.csv", "length": 440, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36339", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J104448.70-363200.2.csv", "length": 651, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36356", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53907883.csv", "length": 139, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36359", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35326964.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36371", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5880389194131434368.csv", "length": 44, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36361", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35073542.csv", "length": 185, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36369", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31268612.csv", "length": 237, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36375", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59200313.csv", "length": 232, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36413", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2698195182306771328.csv", "length": 49, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36390", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29068061.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36419", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51768323.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36412", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24853126.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36429", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ174325.24+640021.7.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36441", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ043215.89+445333.3.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36431", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ165109.64+304115.9.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36445", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194053.80+183944.3.csv", "length": 146, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36458", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25404785.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36470", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35172381.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36481", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13755242.csv", "length": 138, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36475", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51800159.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36488", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31399587.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36500", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222608.75+570948.1.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36503", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192736.40+290753.5.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36507", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J093856.65-375820.2.csv", "length": 202, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36515", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29669383.csv", "length": 264, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36523", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J081142.60-155900.2.csv", "length": 265, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36538", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ060715.23+643321.4.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36529", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19120047.csv", "length": 270, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36548", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35096700.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36552", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10274228.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36550", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32645404.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36564", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24811386.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36567", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59512778.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36577", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP27870271.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36598", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32903254.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36616", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59186550.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36656", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ044412.96+415915.7.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36668", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ184232.79+294532.9.csv", "length": 270, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46683993.csv", "length": 235, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36686", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ031206.29+584036.8.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36697", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2038327024125788928.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36688", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J040907.50+213102.8.csv", "length": 244, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36700", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34618314.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36690", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19032751.csv", "length": 341, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36694", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ064914.53+035927.1.csv", "length": 87, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36732", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ181201.19+702910.3.csv", "length": 301, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36738", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP17615773.csv", "length": 229, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36749", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34903138.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36767", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ050849.93+424248.7.csv", "length": 111, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36771", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194244.47+194141.4.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36786", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192459.79+431557.9.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36784", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28956681.csv", "length": 178, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36796", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59256838.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36813", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215639.75+610446.2.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36831", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214107.69+374208.8.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36843", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195336.16+532353.6.csv", "length": 78, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36824", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J163403.49+452455.9.csv", "length": 565, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36846", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29939462.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36886", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID2877977187558268544.csv", "length": 52, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36887", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ203544.69+145348.9.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36880", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29371725.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36901", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J002030.73+421309.5.csv", "length": 223, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36909", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28689687.csv", "length": 180, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36942", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28792058.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36969", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5930109213066340480.csv", "length": 45, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36951", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32695665.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36960", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192158.15+244544.2.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36976", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35384114.csv", "length": 208, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36975", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29366020.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_36985", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10021132.csv", "length": 357, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37011", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ181010.61+392311.9.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37008", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221635.17+235834.0.csv", "length": 70, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37019", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10584002.csv", "length": 216, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37034", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29748068.csv", "length": 216, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37060", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59501162.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37050", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9769518.csv", "length": 255, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37049", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ174909.58+274339.7.csv", "length": 299, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37066", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP787605.csv", "length": 225, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37068", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28758172.csv", "length": 195, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37087", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID3033023514088080896.csv", "length": 62, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37094", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192809.93+102027.8.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37097", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192616.01+282748.8.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37106", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29601958.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37112", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP45681378.csv", "length": 236, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37113", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46653465.csv", "length": 269, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37117", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213832.52+281241.0.csv", "length": 101, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37116", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ162524.76+425948.6.csv", "length": 276, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37119", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J004554.97-205842.6.csv", "length": 535, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37128", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31357479.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37134", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5590000957676707840.csv", "length": 50, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37130", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25722298.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37132", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221817.94+524407.8.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37135", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP12102535.csv", "length": 292, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37145", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39732811.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37154", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5241086473236332288.csv", "length": 48, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37157", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224339.90+530931.4.csv", "length": 319, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37177", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J070621.39+114641.6.csv", "length": 146, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37188", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205508.40+531441.5.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37194", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195044.71+222239.9.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37185", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221344.98+445900.5.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37191", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ021440.52+532716.6.csv", "length": 249, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37193", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP11322196.csv", "length": 263, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37209", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ015036.82+551509.1.csv", "length": 124, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37200", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29759363.csv", "length": 157, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37247", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5955011016768267392.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37243", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP20136109.csv", "length": 275, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37265", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29017803.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37263", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221221.08+561610.1.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37280", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24815520.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37285", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195532.70+394505.6.csv", "length": 84, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37290", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J071928.29+083449.9.csv", "length": 146, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37308", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10351142.csv", "length": 369, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37301", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP40475556.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37305", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ023638.61+621053.5.csv", "length": 240, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37321", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194652.16+105501.1.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37312", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58691477.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37338", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J145344.01-705153.9.csv", "length": 153, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37344", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29876300.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37353", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ204219.24+382753.7.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37351", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34913205.csv", "length": 213, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37370", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID4530970564901775616.csv", "length": 83, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37365", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J144455.72+004745.0.csv", "length": 197, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37361", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214911.09+561101.4.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37369", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP57912090.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37397", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ212527.02+582903.0.csv", "length": 324, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37391", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205253.45+465226.9.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37454", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180325.71+190332.7.csv", "length": 246, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37467", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46577906.csv", "length": 251, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37504", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J183621.03-123410.0.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37500", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51757540.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37531", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28878327.csv", "length": 199, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37524", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24882359.csv", "length": 347, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37551", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10605547.csv", "length": 412, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37564", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP6152441.csv", "length": 160, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37565", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32937101.csv", "length": 183, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37574", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194450.41+291602.3.csv", "length": 134, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37582", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5101087169464377856.csv", "length": 65, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37589", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ000233.75+604454.5.csv", "length": 298, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37597", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ064142.86+242236.1.csv", "length": 49, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37604", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP25159544.csv", "length": 174, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37637", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59464217.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37644", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31897149.csv", "length": 142, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP37620882.csv", "length": 305, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37645", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32921249.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37653", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24835599.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37672", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29895040.csv", "length": 218, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37659", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J075953.64-104547.1.csv", "length": 286, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37668", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ143607.83+343455.2.csv", "length": 367, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37691", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ111332.16+661226.0.csv", "length": 71, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37681", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29790903.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37707", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP20523501.csv", "length": 247, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37709", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ044628.77+441529.9.csv", "length": 119, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37720", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10467470.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37738", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29545335.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37749", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222244.82+451228.6.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37755", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9534000.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37760", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210551.86+125334.5.csv", "length": 152, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37752", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180326.28+333659.1.csv", "length": 481, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37768", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224406.89+553430.9.csv", "length": 324, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37767", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225052.90+544215.8.csv", "length": 159, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37784", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195317.20+595941.1.csv", "length": 122, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37782", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195033.04+332948.0.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37822", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59485130.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37846", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32636830.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37835", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220710.27+472634.6.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37858", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ233238.97+693332.1.csv", "length": 63, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37853", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32920511.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37857", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51738066.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37864", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J065847.25+062036.9.csv", "length": 177, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37884", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ032628.44+624931.1.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37913", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35293900.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37912", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215641.25+441907.2.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37922", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ171043.78+392836.0.csv", "length": 262, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37938", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31929840.csv", "length": 142, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37955", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32808336.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37987", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ173817.30+231145.9.csv", "length": 223, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38009", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ014600.76+584550.5.csv", "length": 254, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38028", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51664226.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38042", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194910.37+111400.7.csv", "length": 61, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_37998", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J091801.09-073744.2.csv", "length": 1109, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38039", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185743.71+130800.6.csv", "length": 153, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38034", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193826.38+151206.3.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38046", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18899208.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38062", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5983396425582947968.csv", "length": 55, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38071", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194440.12+293159.0.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38082", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J182352.81-303446.9.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38080", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190631.01-002706.5.csv", "length": 88, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38105", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30018068.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38115", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193110.81+292851.5.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38209", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195550.32+210852.4.csv", "length": 149, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38193", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24895213.csv", "length": 118, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38202", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J052333.56+444536.3.csv", "length": 226, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38219", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51694049.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38227", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055346.54+293856.1.csv", "length": 158, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38246", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID6903246307173663488.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as RR Lyrae.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["RR Lyrae"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38248", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51694050.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38252", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32990694.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38298", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193419.48+125218.9.csv", "length": 133, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38316", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29899407.csv", "length": 210, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38312", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32702774.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38326", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21451850.csv", "length": 254, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38332", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ004652.91+502011.8.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38342", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30042567.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38350", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51732435.csv", "length": 124, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38355", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195207.76+313804.8.csv", "length": 172, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38361", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J063837.13-174301.5.csv", "length": 172, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38362", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39794351.csv", "length": 367, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38374", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ224423.93+683502.5.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38380", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221558.48+543503.5.csv", "length": 168, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38379", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ223248.41+550444.9.csv", "length": 165, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38407", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215157.51+495100.3.csv", "length": 153, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38398", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30085907.csv", "length": 207, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38403", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP18822544.csv", "length": 257, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38424", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59472703.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38434", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195453.04+372740.8.csv", "length": 80, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38432", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ220630.16+630727.9.csv", "length": 173, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38448", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ065435.64+001452.1.csv", "length": 140, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38452", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200055.13+171838.9.csv", "length": 64, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38457", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP40475566.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38478", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ161940.21+025157.2.csv", "length": 57, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38486", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ022315.78+354010.4.csv", "length": 91, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38505", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230728.30+590944.2.csv", "length": 312, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38556", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59421438.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38539", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP40564789.csv", "length": 384, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38551", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10383290.csv", "length": 104, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38547", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J050553.31-680707.6.csv", "length": 440, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38571", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J053530.16-061206.0.csv", "length": 268, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38557", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30609692.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38569", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192305.55+223015.6.csv", "length": 331, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38582", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP45424180.csv", "length": 228, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38591", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195324.53+213640.1.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38590", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ152308.63+475752.2.csv", "length": 269, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38609", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211946.85+543129.8.csv", "length": 144, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38617", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211108.48+130352.8.csv", "length": 72, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38618", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29983243.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38615", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29384223.csv", "length": 101, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38610", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP2135598.csv", "length": 450, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38632", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP24826264.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38647", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5701891452804411520.csv", "length": 46, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38658", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ024212.05+573438.1.csv", "length": 117, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38654", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35272695.csv", "length": 123, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38652", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30005575.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38669", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J053802.61-020444.5.csv", "length": 242, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59593296.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38684", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ141935.11+174148.0.csv", "length": 71, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38698", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ232849.07+550638.4.csv", "length": 148, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38697", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51652825.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38702", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ190432.99+531727.9.csv", "length": 103, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38695", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10385371.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38713", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ000314.44+563514.0.csv", "length": 196, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38714", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J190602.22-631012.6.csv", "length": 371, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38719", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32921293.csv", "length": 186, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38768", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ003407.01+622946.6.csv", "length": 142, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38775", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J000839.60-760721.0.csv", "length": 487, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38790", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10523324.csv", "length": 190, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38827", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10896733.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38857", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5593804065317919744.csv", "length": 49, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38851", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5249750934083165312.csv", "length": 47, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Long-Period Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Long-Period Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38879", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10439742.csv", "length": 125, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38875", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ235852.75+644207.1.csv", "length": 278, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38881", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191649.10+274945.6.csv", "length": 241, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38888", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46985884.csv", "length": 285, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38908", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34716200.csv", "length": 150, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38909", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J071701.15-135729.6.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38918", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP34979593.csv", "length": 259, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38892", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51631445.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38921", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP58769338.csv", "length": 473, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38922", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J221525.99+253155.9.csv", "length": 419, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38951", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J100713.37-662419.1.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38953", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35003966.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_38969", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ030040.50+575527.9.csv", "length": 220, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39023", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29892993.csv", "length": 214, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39032", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59296461.csv", "length": 250, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39044", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191723.84+092204.5.csv", "length": 138, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39048", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230839.22+531707.1.csv", "length": 160, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39055", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29840734.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39083", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ193733.10+341921.6.csv", "length": 258, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39103", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID5539084292182237952.csv", "length": 51, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39095", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP28578452.csv", "length": 295, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39120", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J182541.03-265435.6.csv", "length": 120, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39118", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ205756.93+220132.3.csv", "length": 141, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39123", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35093212.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39113", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ160950.63+462844.1.csv", "length": 261, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39139", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32853553.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39161", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19915175.csv", "length": 281, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39168", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J032152.59+440408.3.csv", "length": 121, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39180", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29700671.csv", "length": 271, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39181", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29525151.csv", "length": 441, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39194", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213517.82+462928.1.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39192", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP16575042.csv", "length": 133, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39220", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195352.37+174658.0.csv", "length": 154, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39222", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192648.63+100019.0.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39218", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29514721.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39216", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35219105.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39252", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ222919.41+602200.8.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39259", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ051246.21+365817.9.csv", "length": 204, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39275", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182740.87+144230.6.csv", "length": 59, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39278", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP44544044.csv", "length": 202, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39284", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59518244.csv", "length": 171, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39303", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10563865.csv", "length": 129, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39304", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J115827.66-424328.8.csv", "length": 268, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39333", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35057899.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39355", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ035822.47+592807.6.csv", "length": 60, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39352", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225824.15+554612.1.csv", "length": 156, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39357", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP53363062.csv", "length": 175, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39354", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP39236864.csv", "length": 203, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39358", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ230627.01+564217.7.csv", "length": 160, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39370", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ182731.76+163032.7.csv", "length": 65, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39353", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29601628.csv", "length": 217, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39383", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ184439.85+331000.3.csv", "length": 194, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39396", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201436.04+344243.8.csv", "length": 169, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39412", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ221901.82+604646.0.csv", "length": 163, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39422", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ213846.37+481922.2.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39424", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J065032.37+000025.5.csv", "length": 432, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39448", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ212950.17+121026.5.csv", "length": 74, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Cepheid.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Cepheid"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39437", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185655.14+365234.2.csv", "length": 277, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39445", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9545449.csv", "length": 201, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39442", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP9536971.csv", "length": 207, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39447", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31032706.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39450", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32886227.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39461", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ200812.46+324330.7.csv", "length": 348, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39474", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192638.09+293122.1.csv", "length": 166, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39480", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192549.45+044756.5.csv", "length": 81, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39494", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP48333915.csv", "length": 180, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39521", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ211345.37+460144.2.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39534", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191943.66+425735.0.csv", "length": 82, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39548", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP33174590.csv", "length": 212, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39570", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35191283.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39578", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185559.90+323054.5.csv", "length": 193, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39591", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35008443.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39581", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29781077.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39589", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35020607.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39588", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J211506.36+435005.7.csv", "length": 91, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39606", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ215309.50+685209.9.csv", "length": 67, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39603", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP46759660.csv", "length": 209, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39612", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56828450.csv", "length": 260, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39614", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP51588668.csv", "length": 167, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39628", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ195907.29+212807.8.csv", "length": 211, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39634", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ180424.69+021757.7.csv", "length": 106, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39631", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30015932.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39647", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210005.99+414221.7.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39666", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ185245.80+073302.6.csv", "length": 136, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39664", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ192200.13+052330.9.csv", "length": 127, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39670", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29842478.csv", "length": 181, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39700", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59180968.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39712", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP33011230.csv", "length": 143, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39708", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ055606.04+441412.0.csv", "length": 116, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39729", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ASASSN-V_J042901.42+323532.9.csv", "length": 126, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Delta Scuti.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Delta Scuti"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39711", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35054962.csv", "length": 188, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39743", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ201256.97+464239.9.csv", "length": 80, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39761", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ210800.45+364501.0.csv", "length": 68, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39780", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ125918.74+334025.6.csv", "length": 99, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39792", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ225949.07+584128.0.csv", "length": 164, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39821", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ194114.92+163702.8.csv", "length": 151, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39824", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10951496.csv", "length": 98, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39831", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP19170176.csv", "length": 302, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39829", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP21454307.csv", "length": 254, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39844", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP385257.csv", "length": 200, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39837", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ214143.26+482255.9.csv", "length": 170, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39840", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP10913930.csv", "length": 162, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39865", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29412387.csv", "length": 439, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39866", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ191334.80+333604.2.csv", "length": 82, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39854", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP59157725.csv", "length": 256, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39877", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP56922457.csv", "length": 206, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39894", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/GaiaID3061049343922478208.csv", "length": 38, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Eclipsing Binary.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Eclipsing Binary"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39892", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/ZTFJ040133.84+521425.7.csv", "length": 114, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Rotational Variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Rotational Variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39896", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP29635634.csv", "length": 216, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39911", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP35039954.csv", "length": 96, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39913", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP30025029.csv", "length": 179, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39910", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31412823.csv", "length": 198, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39920", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP31815881.csv", "length": 148, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39939", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32935533.csv", "length": 184, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39928", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP32694704.csv", "length": 370, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "LEAVES", "dataset_name": "LEAVES", "id": "LC_39953", "data_type": "csv", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Astronomy-LEAVES-Light_Curve_Classification/raw_input/AP13678225.csv", "length": 109, "timestamps": [], "fs": 1.1157e-05}, "input_text": "Now, I need your assistance to complete a light curve classification task. It is known that the provided time-series data corresponds to one of the following categories: Cepheid, Delta Scuti, Eclipsing Binary, Long-Period Variable, Non-variable, Rotational Variable or RR Lyrae. Please identify and clearly specify the exact classification result represented by this data.", "gt_text": "This time-series data is classified as Non-variable.", "gt_result": {"class_list": ["Cepheid", "Delta Scuti", "Eclipsing Binary", "Long-Period Variable", "Non-variable", "Rotational Variable", "RR Lyrae"], "gt_class": ["Non-variable"]}, "meta_data": {"ori_task_type": "classification", "ori_domain": "Astronomy", "sub_domain": "Light Curve"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_511_3_15457", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_511_3_15457.npy", "length": 15457, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$511,:3,:15457"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_10_3_15496", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_10_3_15496.npy", "length": 15496, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket1$10,:3,:15496"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_549_3_14000", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_549_3_14000.npy", "length": 14000, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$549,:3,:14000"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_457_3_17054", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_457_3_17054.npy", "length": 17054, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$457,:3,:17054"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_25_3_15094", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_25_3_15094.npy", "length": 15094, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$25,:3,:15094"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_727_3_15242", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_727_3_15242.npy", "length": 15242, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$727,:3,:15242"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_138_3_15582", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_138_3_15582.npy", "length": 15582, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$138,:3,:15582"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_664_3_15320", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_664_3_15320.npy", "length": 15320, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$664,:3,:15320"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_344_3_15232", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_344_3_15232.npy", "length": 15232, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$344,:3,:15232"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_483_3_15307", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_483_3_15307.npy", "length": 15307, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$483,:3,:15307"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_541_3_15386", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_541_3_15386.npy", "length": 15386, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$541,:3,:15386"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_997_3_15063", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_997_3_15063.npy", "length": 15063, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$997,:3,:15063"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_623_3_15831", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_623_3_15831.npy", "length": 15831, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$623,:3,:15831"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_834_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_834_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$834,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_1018_3_15977", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_1018_3_15977.npy", "length": 15977, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$1018,:3,:15977"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_139_3_15311", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_139_3_15311.npy", "length": 15311, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$139,:3,:15311"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_707_3_15569", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_707_3_15569.npy", "length": 15569, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$707,:3,:15569"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_923_3_15264", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_923_3_15264.npy", "length": 15264, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$923,:3,:15264"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_822_3_16493", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_822_3_16493.npy", "length": 16493, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$822,:3,:16493"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_977_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_977_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$977,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_846_3_14945", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_846_3_14945.npy", "length": 14945, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$846,:3,:14945"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_298_3_15369", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_298_3_15369.npy", "length": 15369, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$298,:3,:15369"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_152_3_15414", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_152_3_15414.npy", "length": 15414, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$152,:3,:15414"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_135_3_15142", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_135_3_15142.npy", "length": 15142, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$135,:3,:15142"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_497_3_14878", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_497_3_14878.npy", "length": 14878, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$497,:3,:14878"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_787_3_14589", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_787_3_14589.npy", "length": 14589, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$787,:3,:14589"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_822_3_16357", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_822_3_16357.npy", "length": 16357, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$822,:3,:16357"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_632_3_15752", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_632_3_15752.npy", "length": 15752, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$632,:3,:15752"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_22_3_16418", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_22_3_16418.npy", "length": 16418, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$22,:3,:16418"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_651_3_14710", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_651_3_14710.npy", "length": 14710, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$651,:3,:14710"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_625_3_15323", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_625_3_15323.npy", "length": 15323, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$625,:3,:15323"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_822_3_14691", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_822_3_14691.npy", "length": 14691, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$822,:3,:14691"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_928_3_15515", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_928_3_15515.npy", "length": 15515, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$928,:3,:15515"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_142_3_15753", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_142_3_15753.npy", "length": 15753, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$142,:3,:15753"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_769_3_14837", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_769_3_14837.npy", "length": 14837, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$769,:3,:14837"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_99_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_99_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$99,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_862_3_15598", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_862_3_15598.npy", "length": 15598, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$862,:3,:15598"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_932_3_15660", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_932_3_15660.npy", "length": 15660, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$932,:3,:15660"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_790_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_790_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$790,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_366_3_16587", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_366_3_16587.npy", "length": 16587, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$366,:3,:16587"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_29_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_29_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$29,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_363_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_363_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$363,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_992_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_992_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$992,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_802_3_14980", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_802_3_14980.npy", "length": 14980, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$802,:3,:14980"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_377_3_15229", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_377_3_15229.npy", "length": 15229, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$377,:3,:15229"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_772_3_15856", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_772_3_15856.npy", "length": 15856, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$772,:3,:15856"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_669_3_15174", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_669_3_15174.npy", "length": 15174, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$669,:3,:15174"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_137_3_15389", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_137_3_15389.npy", "length": 15389, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$137,:3,:15389"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_433_3_15716", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_433_3_15716.npy", "length": 15716, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$433,:3,:15716"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_412_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_412_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$412,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_768_3_14741", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_768_3_14741.npy", "length": 14741, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$768,:3,:14741"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_38_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_38_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$38,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_461_3_15013", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_461_3_15013.npy", "length": 15013, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$461,:3,:15013"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_208_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_208_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$208,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_549_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_549_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$549,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_215_3_15384", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_215_3_15384.npy", "length": 15384, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$215,:3,:15384"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_763_3_17324", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_763_3_17324.npy", "length": 17324, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$763,:3,:17324"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_638_3_14820", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_638_3_14820.npy", "length": 14820, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$638,:3,:14820"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_379_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_379_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$379,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_420_3_15252", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_420_3_15252.npy", "length": 15252, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$420,:3,:15252"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_310_3_17488", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_310_3_17488.npy", "length": 17488, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$310,:3,:17488"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_287_3_16112", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_287_3_16112.npy", "length": 16112, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$287,:3,:16112"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_250_3_15311", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_250_3_15311.npy", "length": 15311, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$250,:3,:15311"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_555_3_16114", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_555_3_16114.npy", "length": 16114, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$555,:3,:16114"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_494_3_14881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_494_3_14881.npy", "length": 14881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$494,:3,:14881"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_17_3_15304", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_17_3_15304.npy", "length": 15304, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$17,:3,:15304"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_707_3_15822", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_707_3_15822.npy", "length": 15822, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$707,:3,:15822"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_86_3_15700", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_86_3_15700.npy", "length": 15700, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$86,:3,:15700"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_909_3_16253", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_909_3_16253.npy", "length": 16253, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$909,:3,:16253"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_96_3_15354", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_96_3_15354.npy", "length": 15354, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$96,:3,:15354"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_803_3_15940", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_803_3_15940.npy", "length": 15940, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$803,:3,:15940"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_328_3_16000", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_328_3_16000.npy", "length": 16000, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$328,:3,:16000"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_792_3_15413", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_792_3_15413.npy", "length": 15413, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$792,:3,:15413"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_732_3_15198", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_732_3_15198.npy", "length": 15198, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$732,:3,:15198"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_162_3_16314", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_162_3_16314.npy", "length": 16314, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$162,:3,:16314"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_261_3_15618", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_261_3_15618.npy", "length": 15618, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$261,:3,:15618"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_8_3_16035", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_8_3_16035.npy", "length": 16035, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$8,:3,:16035"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_612_3_15738", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_612_3_15738.npy", "length": 15738, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$612,:3,:15738"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_467_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_467_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$467,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_204_3_15854", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_204_3_15854.npy", "length": 15854, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$204,:3,:15854"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_853_3_15907", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_853_3_15907.npy", "length": 15907, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$853,:3,:15907"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_118_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_118_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$118,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_997_3_16567", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_997_3_16567.npy", "length": 16567, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$997,:3,:16567"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_935_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_935_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$935,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_553_3_16060", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_553_3_16060.npy", "length": 16060, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$553,:3,:16060"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_828_3_15092", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_828_3_15092.npy", "length": 15092, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$828,:3,:15092"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_969_3_15791", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_969_3_15791.npy", "length": 15791, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$969,:3,:15791"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_362_3_16433", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_362_3_16433.npy", "length": 16433, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$362,:3,:16433"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_322_3_15052", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_322_3_15052.npy", "length": 15052, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$322,:3,:15052"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_441_3_15369", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_441_3_15369.npy", "length": 15369, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$441,:3,:15369"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_79_3_15742", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_79_3_15742.npy", "length": 15742, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$79,:3,:15742"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_344_3_15858", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_344_3_15858.npy", "length": 15858, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$344,:3,:15858"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_914_3_15645", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_914_3_15645.npy", "length": 15645, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$914,:3,:15645"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_444_3_15603", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_444_3_15603.npy", "length": 15603, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$444,:3,:15603"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_27_3_15280", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_27_3_15280.npy", "length": 15280, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$27,:3,:15280"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_879_3_16298", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_879_3_16298.npy", "length": 16298, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$879,:3,:16298"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_982_3_16433", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_982_3_16433.npy", "length": 16433, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$982,:3,:16433"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_622_3_15148", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_622_3_15148.npy", "length": 15148, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$622,:3,:15148"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_476_3_14795", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_476_3_14795.npy", "length": 14795, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$476,:3,:14795"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_240_3_15555", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_240_3_15555.npy", "length": 15555, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$240,:3,:15555"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_197_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_197_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$197,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_827_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_827_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$827,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_562_3_15469", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_562_3_15469.npy", "length": 15469, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$562,:3,:15469"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_859_3_16339", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_859_3_16339.npy", "length": 16339, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$859,:3,:16339"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_97_3_16891", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_97_3_16891.npy", "length": 16891, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$97,:3,:16891"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_58_3_14804", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_58_3_14804.npy", "length": 14804, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$58,:3,:14804"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_990_3_15445", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_990_3_15445.npy", "length": 15445, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket1$990,:3,:15445"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_140_3_15130", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_140_3_15130.npy", "length": 15130, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$140,:3,:15130"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_120_3_16150", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_120_3_16150.npy", "length": 16150, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$120,:3,:16150"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_396_3_15967", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_396_3_15967.npy", "length": 15967, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$396,:3,:15967"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_182_3_14520", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_182_3_14520.npy", "length": 14520, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$182,:3,:14520"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_637_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_637_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$637,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_519_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_519_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$519,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_917_3_15189", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_917_3_15189.npy", "length": 15189, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$917,:3,:15189"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_933_3_15092", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_933_3_15092.npy", "length": 15092, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$933,:3,:15092"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_530_3_14795", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_530_3_14795.npy", "length": 14795, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$530,:3,:14795"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_175_3_14897", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_175_3_14897.npy", "length": 14897, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$175,:3,:14897"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_264_3_15059", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_264_3_15059.npy", "length": 15059, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$264,:3,:15059"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_936_3_14895", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_936_3_14895.npy", "length": 14895, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$936,:3,:14895"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_469_3_15408", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_469_3_15408.npy", "length": 15408, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$469,:3,:15408"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_340_3_16098", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_340_3_16098.npy", "length": 16098, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$340,:3,:16098"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_1012_3_16232", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_1012_3_16232.npy", "length": 16232, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$1012,:3,:16232"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_120_3_15147", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_120_3_15147.npy", "length": 15147, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$120,:3,:15147"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_948_3_16660", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_948_3_16660.npy", "length": 16660, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$948,:3,:16660"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_1_3_15456", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_1_3_15456.npy", "length": 15456, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$1,:3,:15456"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_799_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_799_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$799,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_145_3_15947", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_145_3_15947.npy", "length": 15947, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$145,:3,:15947"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_188_3_15798", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_188_3_15798.npy", "length": 15798, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$188,:3,:15798"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_855_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_855_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$855,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_726_3_15022", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_726_3_15022.npy", "length": 15022, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$726,:3,:15022"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_209_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_209_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$209,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_69_3_15022", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_69_3_15022.npy", "length": 15022, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$69,:3,:15022"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_189_3_15169", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_189_3_15169.npy", "length": 15169, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$189,:3,:15169"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_669_3_14948", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_669_3_14948.npy", "length": 14948, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$669,:3,:14948"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_796_3_14964", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_796_3_14964.npy", "length": 14964, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$796,:3,:14964"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_811_3_15766", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_811_3_15766.npy", "length": 15766, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$811,:3,:15766"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_938_3_16273", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_938_3_16273.npy", "length": 16273, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$938,:3,:16273"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_683_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_683_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$683,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_232_3_15945", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_232_3_15945.npy", "length": 15945, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$232,:3,:15945"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_860_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_860_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$860,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_811_3_16396", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_811_3_16396.npy", "length": 16396, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$811,:3,:16396"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_1013_3_14953", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_1013_3_14953.npy", "length": 14953, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$1013,:3,:14953"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_655_3_14814", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_655_3_14814.npy", "length": 14814, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$655,:3,:14814"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_366_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_366_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$366,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_394_3_16160", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_394_3_16160.npy", "length": 16160, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$394,:3,:16160"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_276_3_14812", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_276_3_14812.npy", "length": 14812, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$276,:3,:14812"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_703_3_16248", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_703_3_16248.npy", "length": 16248, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$703,:3,:16248"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_942_3_16024", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_942_3_16024.npy", "length": 16024, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$942,:3,:16024"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_872_3_16265", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_872_3_16265.npy", "length": 16265, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$872,:3,:16265"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_16_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_16_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$16,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_465_3_14587", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_465_3_14587.npy", "length": 14587, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$465,:3,:14587"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_1013_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_1013_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$1013,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_600_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_600_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$600,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_679_3_15516", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_679_3_15516.npy", "length": 15516, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$679,:3,:15516"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_72_3_15069", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_72_3_15069.npy", "length": 15069, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$72,:3,:15069"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_926_3_16417", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_926_3_16417.npy", "length": 16417, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$926,:3,:16417"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_925_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_925_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$925,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_728_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_728_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$728,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_464_3_16339", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_464_3_16339.npy", "length": 16339, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$464,:3,:16339"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_869_3_14881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_869_3_14881.npy", "length": 14881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$869,:3,:14881"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_589_3_15686", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_589_3_15686.npy", "length": 15686, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$589,:3,:15686"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_782_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_782_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$782,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_644_3_16808", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_644_3_16808.npy", "length": 16808, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$644,:3,:16808"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_892_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_892_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$892,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_78_3_15332", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_78_3_15332.npy", "length": 15332, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket4$78,:3,:15332"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_620_3_15689", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_620_3_15689.npy", "length": 15689, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket9$620,:3,:15689"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_719_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_719_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$719,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_446_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_446_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$446,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_680_3_15507", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_680_3_15507.npy", "length": 15507, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$680,:3,:15507"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_687_3_16416", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_687_3_16416.npy", "length": 16416, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$687,:3,:16416"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_523_3_16597", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_523_3_16597.npy", "length": 16597, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$523,:3,:16597"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_739_3_16494", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_739_3_16494.npy", "length": 16494, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$739,:3,:16494"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_441_3_15450", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_441_3_15450.npy", "length": 15450, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$441,:3,:15450"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_1015_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_1015_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$1015,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_879_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_879_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$879,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_881_3_16232", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_881_3_16232.npy", "length": 16232, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$881,:3,:16232"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_936_3_15257", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_936_3_15257.npy", "length": 15257, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$936,:3,:15257"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_601_3_15978", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_601_3_15978.npy", "length": 15978, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$601,:3,:15978"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_413_3_15519", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_413_3_15519.npy", "length": 15519, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$413,:3,:15519"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_535_3_15286", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_535_3_15286.npy", "length": 15286, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$535,:3,:15286"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_429_3_15800", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_429_3_15800.npy", "length": 15800, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$429,:3,:15800"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_929_3_15898", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_929_3_15898.npy", "length": 15898, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$929,:3,:15898"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_229_3_14844", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_229_3_14844.npy", "length": 14844, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$229,:3,:14844"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_890_3_15823", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_890_3_15823.npy", "length": 15823, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket9$890,:3,:15823"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_68_3_15831", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_68_3_15831.npy", "length": 15831, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$68,:3,:15831"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_386_3_15122", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_386_3_15122.npy", "length": 15122, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$386,:3,:15122"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_263_3_16716", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_263_3_16716.npy", "length": 16716, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$263,:3,:16716"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_411_3_16220", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_411_3_16220.npy", "length": 16220, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$411,:3,:16220"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_398_3_15167", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_398_3_15167.npy", "length": 15167, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$398,:3,:15167"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_142_3_17117", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_142_3_17117.npy", "length": 17117, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$142,:3,:17117"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_785_3_15130", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_785_3_15130.npy", "length": 15130, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$785,:3,:15130"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_238_3_15691", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_238_3_15691.npy", "length": 15691, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$238,:3,:15691"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_890_3_16407", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_890_3_16407.npy", "length": 16407, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$890,:3,:16407"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_816_3_15084", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_816_3_15084.npy", "length": 15084, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$816,:3,:15084"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_1011_3_16737", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_1011_3_16737.npy", "length": 16737, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$1011,:3,:16737"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_680_3_14427", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_680_3_14427.npy", "length": 14427, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$680,:3,:14427"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_490_3_17471", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_490_3_17471.npy", "length": 17471, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$490,:3,:17471"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_547_3_14613", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_547_3_14613.npy", "length": 14613, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$547,:3,:14613"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_779_3_15470", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_779_3_15470.npy", "length": 15470, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$779,:3,:15470"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_98_3_14900", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_98_3_14900.npy", "length": 14900, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$98,:3,:14900"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_192_3_14799", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_192_3_14799.npy", "length": 14799, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$192,:3,:14799"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_368_3_15775", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_368_3_15775.npy", "length": 15775, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$368,:3,:15775"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_431_3_14880", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_431_3_14880.npy", "length": 14880, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$431,:3,:14880"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_80_3_15031", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_80_3_15031.npy", "length": 15031, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket4$80,:3,:15031"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_406_3_15986", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_406_3_15986.npy", "length": 15986, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$406,:3,:15986"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_61_3_15695", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_61_3_15695.npy", "length": 15695, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$61,:3,:15695"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_16_3_15586", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_16_3_15586.npy", "length": 15586, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$16,:3,:15586"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_441_3_14642", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_441_3_14642.npy", "length": 14642, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$441,:3,:14642"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_939_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_939_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$939,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_72_3_15937", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_72_3_15937.npy", "length": 15937, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$72,:3,:15937"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_253_3_15531", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_253_3_15531.npy", "length": 15531, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$253,:3,:15531"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_1008_3_16213", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_1008_3_16213.npy", "length": 16213, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$1008,:3,:16213"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_413_3_16681", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_413_3_16681.npy", "length": 16681, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$413,:3,:16681"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_578_3_15348", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_578_3_15348.npy", "length": 15348, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$578,:3,:15348"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_325_3_14767", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_325_3_14767.npy", "length": 14767, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$325,:3,:14767"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_330_3_14789", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_330_3_14789.npy", "length": 14789, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$330,:3,:14789"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_83_3_15407", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_83_3_15407.npy", "length": 15407, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$83,:3,:15407"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_49_3_15379", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_49_3_15379.npy", "length": 15379, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$49,:3,:15379"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_196_3_15007", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_196_3_15007.npy", "length": 15007, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$196,:3,:15007"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_898_3_16182", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_898_3_16182.npy", "length": 16182, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$898,:3,:16182"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_147_3_16699", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_147_3_16699.npy", "length": 16699, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$147,:3,:16699"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_345_3_15730", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_345_3_15730.npy", "length": 15730, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$345,:3,:15730"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_131_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_131_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$131,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_661_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_661_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$661,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_164_3_14776", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_164_3_14776.npy", "length": 14776, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$164,:3,:14776"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_664_3_16574", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_664_3_16574.npy", "length": 16574, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$664,:3,:16574"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_659_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_659_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$659,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_351_3_14988", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_351_3_14988.npy", "length": 14988, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$351,:3,:14988"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_306_3_15477", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_306_3_15477.npy", "length": 15477, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$306,:3,:15477"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_986_3_15625", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_986_3_15625.npy", "length": 15625, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$986,:3,:15625"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_122_3_15282", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_122_3_15282.npy", "length": 15282, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$122,:3,:15282"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_223_3_15570", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_223_3_15570.npy", "length": 15570, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$223,:3,:15570"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_955_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_955_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$955,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_295_3_15795", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_295_3_15795.npy", "length": 15795, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$295,:3,:15795"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_662_3_15854", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_662_3_15854.npy", "length": 15854, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$662,:3,:15854"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_317_3_15338", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_317_3_15338.npy", "length": 15338, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$317,:3,:15338"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_157_3_15267", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_157_3_15267.npy", "length": 15267, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$157,:3,:15267"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_550_3_15177", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_550_3_15177.npy", "length": 15177, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$550,:3,:15177"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_201_3_15951", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_201_3_15951.npy", "length": 15951, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$201,:3,:15951"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_992_3_14944", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_992_3_14944.npy", "length": 14944, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$992,:3,:14944"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_794_3_16342", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_794_3_16342.npy", "length": 16342, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$794,:3,:16342"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_783_3_16256", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_783_3_16256.npy", "length": 16256, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$783,:3,:16256"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_514_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_514_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$514,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_371_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_371_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$371,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_493_3_15427", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_493_3_15427.npy", "length": 15427, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$493,:3,:15427"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_605_3_15633", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_605_3_15633.npy", "length": 15633, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$605,:3,:15633"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_896_3_15404", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_896_3_15404.npy", "length": 15404, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$896,:3,:15404"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_274_3_15126", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_274_3_15126.npy", "length": 15126, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$274,:3,:15126"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_647_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_647_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$647,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_886_3_15007", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_886_3_15007.npy", "length": 15007, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$886,:3,:15007"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_685_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_685_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$685,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_91_3_15014", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_91_3_15014.npy", "length": 15014, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$91,:3,:15014"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_153_3_16279", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_153_3_16279.npy", "length": 16279, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$153,:3,:16279"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_166_3_15488", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_166_3_15488.npy", "length": 15488, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$166,:3,:15488"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_867_3_15329", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_867_3_15329.npy", "length": 15329, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$867,:3,:15329"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_1023_3_15979", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_1023_3_15979.npy", "length": 15979, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$1023,:3,:15979"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_514_3_14870", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_514_3_14870.npy", "length": 14870, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$514,:3,:14870"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_187_3_15652", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_187_3_15652.npy", "length": 15652, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$187,:3,:15652"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_815_3_17040", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_815_3_17040.npy", "length": 17040, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$815,:3,:17040"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_955_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_955_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$955,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_839_3_15826", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_839_3_15826.npy", "length": 15826, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$839,:3,:15826"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_512_3_14743", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_512_3_14743.npy", "length": 14743, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$512,:3,:14743"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_932_3_15782", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_932_3_15782.npy", "length": 15782, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$932,:3,:15782"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_534_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_534_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$534,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_633_3_16327", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_633_3_16327.npy", "length": 16327, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$633,:3,:16327"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_931_3_15151", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_931_3_15151.npy", "length": 15151, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$931,:3,:15151"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_752_3_16089", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_752_3_16089.npy", "length": 16089, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$752,:3,:16089"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_91_3_15594", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_91_3_15594.npy", "length": 15594, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket3$91,:3,:15594"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_814_3_14976", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_814_3_14976.npy", "length": 14976, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$814,:3,:14976"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_29_3_15881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_29_3_15881.npy", "length": 15881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$29,:3,:15881"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_384_3_14000", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_384_3_14000.npy", "length": 14000, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$384,:3,:14000"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_497_3_16922", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_497_3_16922.npy", "length": 16922, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$497,:3,:16922"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_673_3_15569", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_673_3_15569.npy", "length": 15569, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$673,:3,:15569"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_735_3_15230", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_735_3_15230.npy", "length": 15230, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$735,:3,:15230"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "13_58_3_14712", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/13_58_3_14712.npy", "length": 14712, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket13$58,:3,:14712"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_48_3_15163", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_48_3_15163.npy", "length": 15163, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$48,:3,:15163"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_167_3_15060", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_167_3_15060.npy", "length": 15060, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$167,:3,:15060"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_962_3_14848", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_962_3_14848.npy", "length": 14848, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$962,:3,:14848"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_546_3_15817", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_546_3_15817.npy", "length": 15817, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$546,:3,:15817"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_235_3_14726", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_235_3_14726.npy", "length": 14726, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$235,:3,:14726"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_540_3_14723", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_540_3_14723.npy", "length": 14723, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$540,:3,:14723"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_843_3_14936", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_843_3_14936.npy", "length": 14936, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$843,:3,:14936"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_405_3_15332", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_405_3_15332.npy", "length": 15332, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$405,:3,:15332"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_640_3_14766", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_640_3_14766.npy", "length": 14766, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$640,:3,:14766"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_985_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_985_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$985,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_348_3_15511", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_348_3_15511.npy", "length": 15511, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$348,:3,:15511"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_23_3_15626", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_23_3_15626.npy", "length": 15626, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$23,:3,:15626"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_905_3_14917", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_905_3_14917.npy", "length": 14917, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$905,:3,:14917"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_934_3_15411", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_934_3_15411.npy", "length": 15411, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$934,:3,:15411"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_266_3_16073", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_266_3_16073.npy", "length": 16073, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$266,:3,:16073"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_546_3_14753", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_546_3_14753.npy", "length": 14753, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$546,:3,:14753"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_672_3_16373", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_672_3_16373.npy", "length": 16373, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$672,:3,:16373"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_890_3_15967", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_890_3_15967.npy", "length": 15967, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$890,:3,:15967"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_579_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_579_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$579,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_229_3_15789", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_229_3_15789.npy", "length": 15789, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$229,:3,:15789"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_755_3_15685", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_755_3_15685.npy", "length": 15685, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$755,:3,:15685"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_45_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_45_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$45,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_59_3_16045", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_59_3_16045.npy", "length": 16045, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$59,:3,:16045"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_926_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_926_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$926,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_822_3_15125", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_822_3_15125.npy", "length": 15125, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$822,:3,:15125"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_906_3_15917", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_906_3_15917.npy", "length": 15917, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$906,:3,:15917"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_312_3_15469", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_312_3_15469.npy", "length": 15469, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$312,:3,:15469"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_841_3_14673", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_841_3_14673.npy", "length": 14673, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$841,:3,:14673"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_268_3_15735", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_268_3_15735.npy", "length": 15735, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$268,:3,:15735"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_702_3_16640", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_702_3_16640.npy", "length": 16640, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$702,:3,:16640"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_545_3_15236", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_545_3_15236.npy", "length": 15236, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$545,:3,:15236"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_1022_3_15642", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_1022_3_15642.npy", "length": 15642, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$1022,:3,:15642"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_888_3_15310", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_888_3_15310.npy", "length": 15310, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$888,:3,:15310"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_586_3_16008", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_586_3_16008.npy", "length": 16008, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$586,:3,:16008"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_724_3_15728", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_724_3_15728.npy", "length": 15728, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$724,:3,:15728"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_559_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_559_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$559,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_1013_3_15830", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_1013_3_15830.npy", "length": 15830, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$1013,:3,:15830"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_172_3_14773", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_172_3_14773.npy", "length": 14773, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$172,:3,:14773"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_211_3_14563", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_211_3_14563.npy", "length": 14563, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$211,:3,:14563"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_542_3_15535", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_542_3_15535.npy", "length": 15535, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$542,:3,:15535"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_382_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_382_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$382,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_808_3_15723", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_808_3_15723.npy", "length": 15723, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$808,:3,:15723"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_133_3_15914", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_133_3_15914.npy", "length": 15914, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$133,:3,:15914"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_6_3_14856", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_6_3_14856.npy", "length": 14856, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$6,:3,:14856"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_954_3_15998", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_954_3_15998.npy", "length": 15998, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$954,:3,:15998"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_554_3_15274", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_554_3_15274.npy", "length": 15274, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$554,:3,:15274"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_185_3_14881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_185_3_14881.npy", "length": 14881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$185,:3,:14881"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_548_3_15645", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_548_3_15645.npy", "length": 15645, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$548,:3,:15645"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_468_3_16145", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_468_3_16145.npy", "length": 16145, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$468,:3,:16145"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_650_3_14860", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_650_3_14860.npy", "length": 14860, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$650,:3,:14860"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_423_3_17463", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_423_3_17463.npy", "length": 17463, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$423,:3,:17463"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_409_3_16080", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_409_3_16080.npy", "length": 16080, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$409,:3,:16080"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_284_3_14704", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_284_3_14704.npy", "length": 14704, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$284,:3,:14704"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_221_3_14948", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_221_3_14948.npy", "length": 14948, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$221,:3,:14948"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_60_3_16515", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_60_3_16515.npy", "length": 16515, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$60,:3,:16515"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_962_3_15420", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_962_3_15420.npy", "length": 15420, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$962,:3,:15420"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_471_3_15430", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_471_3_15430.npy", "length": 15430, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$471,:3,:15430"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_809_3_15382", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_809_3_15382.npy", "length": 15382, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$809,:3,:15382"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_319_3_15527", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_319_3_15527.npy", "length": 15527, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$319,:3,:15527"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_444_3_15281", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_444_3_15281.npy", "length": 15281, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$444,:3,:15281"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_369_3_16102", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_369_3_16102.npy", "length": 16102, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$369,:3,:16102"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_664_3_15035", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_664_3_15035.npy", "length": 15035, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$664,:3,:15035"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_96_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_96_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$96,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_763_3_15978", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_763_3_15978.npy", "length": 15978, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$763,:3,:15978"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_102_3_16044", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_102_3_16044.npy", "length": 16044, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$102,:3,:16044"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_766_3_14992", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_766_3_14992.npy", "length": 14992, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$766,:3,:14992"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_459_3_16761", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_459_3_16761.npy", "length": 16761, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$459,:3,:16761"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_148_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_148_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$148,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_591_3_17439", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_591_3_17439.npy", "length": 17439, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$591,:3,:17439"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_229_3_15722", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_229_3_15722.npy", "length": 15722, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$229,:3,:15722"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_42_3_15117", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_42_3_15117.npy", "length": 15117, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$42,:3,:15117"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_537_3_15739", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_537_3_15739.npy", "length": 15739, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$537,:3,:15739"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_173_3_15188", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_173_3_15188.npy", "length": 15188, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$173,:3,:15188"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_391_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_391_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$391,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_518_3_14962", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_518_3_14962.npy", "length": 14962, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$518,:3,:14962"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_19_3_15157", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_19_3_15157.npy", "length": 15157, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$19,:3,:15157"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_606_3_16524", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_606_3_16524.npy", "length": 16524, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket11$606,:3,:16524"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_483_3_16073", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_483_3_16073.npy", "length": 16073, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$483,:3,:16073"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_312_3_15839", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_312_3_15839.npy", "length": 15839, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$312,:3,:15839"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_496_3_14657", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_496_3_14657.npy", "length": 14657, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$496,:3,:14657"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_598_3_15284", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_598_3_15284.npy", "length": 15284, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$598,:3,:15284"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_920_3_14986", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_920_3_14986.npy", "length": 14986, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$920,:3,:14986"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_328_3_15435", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_328_3_15435.npy", "length": 15435, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$328,:3,:15435"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_698_3_17196", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_698_3_17196.npy", "length": 17196, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$698,:3,:17196"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_326_3_15551", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_326_3_15551.npy", "length": 15551, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$326,:3,:15551"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_171_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_171_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$171,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_197_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_197_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$197,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_416_3_16166", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_416_3_16166.npy", "length": 16166, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$416,:3,:16166"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_760_3_15322", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_760_3_15322.npy", "length": 15322, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$760,:3,:15322"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_281_3_15502", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_281_3_15502.npy", "length": 15502, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$281,:3,:15502"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_206_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_206_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$206,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_785_3_15595", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_785_3_15595.npy", "length": 15595, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$785,:3,:15595"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_498_3_16370", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_498_3_16370.npy", "length": 16370, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$498,:3,:16370"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_887_3_15214", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_887_3_15214.npy", "length": 15214, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$887,:3,:15214"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_925_3_14962", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_925_3_14962.npy", "length": 14962, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$925,:3,:14962"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_819_3_16101", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_819_3_16101.npy", "length": 16101, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$819,:3,:16101"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_995_3_15924", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_995_3_15924.npy", "length": 15924, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$995,:3,:15924"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_948_3_16208", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_948_3_16208.npy", "length": 16208, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$948,:3,:16208"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_1006_3_15235", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_1006_3_15235.npy", "length": 15235, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket1$1006,:3,:15235"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_556_3_16256", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_556_3_16256.npy", "length": 16256, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$556,:3,:16256"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_679_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_679_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$679,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_538_3_14736", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_538_3_14736.npy", "length": 14736, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$538,:3,:14736"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_673_3_15231", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_673_3_15231.npy", "length": 15231, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$673,:3,:15231"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_139_3_15473", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_139_3_15473.npy", "length": 15473, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$139,:3,:15473"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_214_3_15823", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_214_3_15823.npy", "length": 15823, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$214,:3,:15823"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_889_3_14686", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_889_3_14686.npy", "length": 14686, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$889,:3,:14686"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_550_3_15048", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_550_3_15048.npy", "length": 15048, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$550,:3,:15048"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_123_3_15260", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_123_3_15260.npy", "length": 15260, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$123,:3,:15260"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_227_3_15356", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_227_3_15356.npy", "length": 15356, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$227,:3,:15356"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_232_3_15594", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_232_3_15594.npy", "length": 15594, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$232,:3,:15594"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_979_3_14692", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_979_3_14692.npy", "length": 14692, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$979,:3,:14692"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_566_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_566_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket9$566,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_98_3_15398", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_98_3_15398.npy", "length": 15398, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$98,:3,:15398"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_836_3_16289", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_836_3_16289.npy", "length": 16289, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$836,:3,:16289"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_588_3_15538", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_588_3_15538.npy", "length": 15538, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$588,:3,:15538"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_830_3_16039", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_830_3_16039.npy", "length": 16039, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$830,:3,:16039"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_406_3_15531", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_406_3_15531.npy", "length": 15531, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$406,:3,:15531"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_161_3_14500", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_161_3_14500.npy", "length": 14500, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$161,:3,:14500"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_1004_3_16067", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_1004_3_16067.npy", "length": 16067, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$1004,:3,:16067"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_87_3_15238", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_87_3_15238.npy", "length": 15238, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$87,:3,:15238"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_346_3_16091", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_346_3_16091.npy", "length": 16091, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$346,:3,:16091"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_730_3_14598", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_730_3_14598.npy", "length": 14598, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$730,:3,:14598"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_269_3_15884", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_269_3_15884.npy", "length": 15884, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$269,:3,:15884"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_194_3_14667", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_194_3_14667.npy", "length": 14667, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$194,:3,:14667"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_37_3_16173", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_37_3_16173.npy", "length": 16173, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$37,:3,:16173"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_252_3_15881", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_252_3_15881.npy", "length": 15881, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$252,:3,:15881"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_232_3_15520", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_232_3_15520.npy", "length": 15520, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$232,:3,:15520"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_510_3_16053", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_510_3_16053.npy", "length": 16053, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$510,:3,:16053"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_751_3_15235", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_751_3_15235.npy", "length": 15235, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$751,:3,:15235"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_949_3_15164", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_949_3_15164.npy", "length": 15164, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$949,:3,:15164"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_425_3_15429", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_425_3_15429.npy", "length": 15429, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$425,:3,:15429"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_158_3_15677", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_158_3_15677.npy", "length": 15677, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$158,:3,:15677"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_23_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_23_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$23,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_240_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_240_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$240,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_28_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_28_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$28,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_216_3_15503", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_216_3_15503.npy", "length": 15503, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$216,:3,:15503"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_832_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_832_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$832,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_446_3_15517", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_446_3_15517.npy", "length": 15517, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$446,:3,:15517"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_1008_3_16536", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_1008_3_16536.npy", "length": 16536, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket11$1008,:3,:16536"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_428_3_14959", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_428_3_14959.npy", "length": 14959, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$428,:3,:14959"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_200_3_16232", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_200_3_16232.npy", "length": 16232, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$200,:3,:16232"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_369_3_15456", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_369_3_15456.npy", "length": 15456, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$369,:3,:15456"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_753_3_15956", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_753_3_15956.npy", "length": 15956, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$753,:3,:15956"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_401_3_14606", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_401_3_14606.npy", "length": 14606, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$401,:3,:14606"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_992_3_15664", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_992_3_15664.npy", "length": 15664, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$992,:3,:15664"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_456_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_456_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$456,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_616_3_15675", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_616_3_15675.npy", "length": 15675, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$616,:3,:15675"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_850_3_15041", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_850_3_15041.npy", "length": 15041, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$850,:3,:15041"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_914_3_15562", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_914_3_15562.npy", "length": 15562, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$914,:3,:15562"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_20_3_15951", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_20_3_15951.npy", "length": 15951, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$20,:3,:15951"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_253_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_253_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$253,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_110_3_16239", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_110_3_16239.npy", "length": 16239, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$110,:3,:16239"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_884_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_884_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$884,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_800_3_15337", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_800_3_15337.npy", "length": 15337, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$800,:3,:15337"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_606_3_15756", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_606_3_15756.npy", "length": 15756, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$606,:3,:15756"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_75_3_15742", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_75_3_15742.npy", "length": 15742, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$75,:3,:15742"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_196_3_15051", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_196_3_15051.npy", "length": 15051, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$196,:3,:15051"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_944_3_16217", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_944_3_16217.npy", "length": 16217, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$944,:3,:16217"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_824_3_14841", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_824_3_14841.npy", "length": 14841, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$824,:3,:14841"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_50_3_15037", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_50_3_15037.npy", "length": 15037, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$50,:3,:15037"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_771_3_15036", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_771_3_15036.npy", "length": 15036, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$771,:3,:15036"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_605_3_15562", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_605_3_15562.npy", "length": 15562, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$605,:3,:15562"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_184_3_15925", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_184_3_15925.npy", "length": 15925, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$184,:3,:15925"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_988_3_15830", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_988_3_15830.npy", "length": 15830, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$988,:3,:15830"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_447_3_15045", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_447_3_15045.npy", "length": 15045, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$447,:3,:15045"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_64_3_14902", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_64_3_14902.npy", "length": 14902, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$64,:3,:14902"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_811_3_14758", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_811_3_14758.npy", "length": 14758, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$811,:3,:14758"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_916_3_15114", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_916_3_15114.npy", "length": 15114, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$916,:3,:15114"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_752_3_15695", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_752_3_15695.npy", "length": 15695, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$752,:3,:15695"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_101_3_15317", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_101_3_15317.npy", "length": 15317, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$101,:3,:15317"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_893_3_15717", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_893_3_15717.npy", "length": 15717, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$893,:3,:15717"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_711_3_15473", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_711_3_15473.npy", "length": 15473, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$711,:3,:15473"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_381_3_16561", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_381_3_16561.npy", "length": 16561, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$381,:3,:16561"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_989_3_15152", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_989_3_15152.npy", "length": 15152, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$989,:3,:15152"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_489_3_14814", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_489_3_14814.npy", "length": 14814, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$489,:3,:14814"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_487_3_15398", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_487_3_15398.npy", "length": 15398, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$487,:3,:15398"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_915_3_14978", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_915_3_14978.npy", "length": 14978, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$915,:3,:14978"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_422_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_422_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$422,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_119_3_14576", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_119_3_14576.npy", "length": 14576, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$119,:3,:14576"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_709_3_16402", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_709_3_16402.npy", "length": 16402, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$709,:3,:16402"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_26_3_15417", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_26_3_15417.npy", "length": 15417, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$26,:3,:15417"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_989_3_15720", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_989_3_15720.npy", "length": 15720, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$989,:3,:15720"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_1007_3_16353", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_1007_3_16353.npy", "length": 16353, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$1007,:3,:16353"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_432_3_15264", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_432_3_15264.npy", "length": 15264, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket10$432,:3,:15264"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_471_3_14916", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_471_3_14916.npy", "length": 14916, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$471,:3,:14916"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_317_3_15169", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_317_3_15169.npy", "length": 15169, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$317,:3,:15169"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_226_3_15463", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_226_3_15463.npy", "length": 15463, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$226,:3,:15463"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_133_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_133_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$133,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_98_3_15095", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_98_3_15095.npy", "length": 15095, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$98,:3,:15095"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_379_3_15609", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_379_3_15609.npy", "length": 15609, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$379,:3,:15609"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_65_3_16067", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_65_3_16067.npy", "length": 16067, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$65,:3,:16067"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_157_3_16289", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_157_3_16289.npy", "length": 16289, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$157,:3,:16289"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_564_3_15192", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_564_3_15192.npy", "length": 15192, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$564,:3,:15192"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_571_3_15491", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_571_3_15491.npy", "length": 15491, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$571,:3,:15491"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_761_3_16544", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_761_3_16544.npy", "length": 16544, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$761,:3,:16544"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_82_3_15727", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_82_3_15727.npy", "length": 15727, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket4$82,:3,:15727"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_770_3_16187", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_770_3_16187.npy", "length": 16187, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$770,:3,:16187"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_231_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_231_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$231,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_33_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_33_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$33,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_729_3_15130", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_729_3_15130.npy", "length": 15130, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$729,:3,:15130"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_838_3_15586", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_838_3_15586.npy", "length": 15586, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$838,:3,:15586"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_460_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_460_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket0$460,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_596_3_15897", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_596_3_15897.npy", "length": 15897, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket9$596,:3,:15897"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_781_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_781_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$781,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_150_3_14675", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_150_3_14675.npy", "length": 14675, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$150,:3,:14675"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "1_152_3_14526", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/1_152_3_14526.npy", "length": 14526, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket1$152,:3,:14526"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_262_3_16148", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_262_3_16148.npy", "length": 16148, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$262,:3,:16148"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "2_163_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/2_163_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket2$163,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_97_3_14807", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_97_3_14807.npy", "length": 14807, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$97,:3,:14807"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_762_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_762_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket8$762,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "10_852_3_15545", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/10_852_3_15545.npy", "length": 15545, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket10$852,:3,:15545"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_499_3_15269", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_499_3_15269.npy", "length": 15269, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$499,:3,:15269"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_462_3_16085", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_462_3_16085.npy", "length": 16085, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$462,:3,:16085"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_353_3_15250", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_353_3_15250.npy", "length": 15250, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket12$353,:3,:15250"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "6_265_3_15648", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/6_265_3_15648.npy", "length": 15648, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket6$265,:3,:15648"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "11_303_3_16570", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/11_303_3_16570.npy", "length": 16570, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket11$303,:3,:16570"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_35_3_14542", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_35_3_14542.npy", "length": 14542, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket5$35,:3,:14542"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "4_919_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/4_919_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket4$919,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_157_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_157_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$157,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "0_887_3_15356", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/0_887_3_15356.npy", "length": 15356, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket0$887,:3,:15356"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "7_698_3_15861", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/7_698_3_15861.npy", "length": 15861, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket7$698,:3,:15861"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "9_19_3_15563", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/9_19_3_15563.npy", "length": 15563, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket9$19,:3,:15563"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "5_305_3_15348", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/5_305_3_15348.npy", "length": 15348, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket5$305,:3,:15348"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "8_501_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/8_501_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket8$501,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_994_3_15541", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_994_3_15541.npy", "length": 15541, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$994,:3,:15541"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "12_322_3_14001", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/12_322_3_14001.npy", "length": 14001, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as intermediate.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["intermediate"]}, "meta_data": {"trace_name": "bucket12$322,:3,:14001"}}
{"task_name": "Iquique", "dataset_name": "Iquique", "id": "3_791_3_15600", "data_type": "npy", "input_ts": {"num_channel": 3, "channel_detail": ["Z", "N", "E"], "path": "Earth_Science-Iquique-Earthquake_Focal_Depth_Classification/raw_input/3_791_3_15600.npy", "length": 15600, "timestamps": [], "fs": 100}, "input_text": "Now I give you a focal depth classification task. Classify the focal depth of the earthquake source into one of the following categories: shallow (0-70 km), intermediate (70-300 km), or deep (300-700 km). Analyze the recording and determine which category it belongs to.", "gt_text": "The focal depth of this seismic event is classified as shallow.", "gt_result": {"class_list": ["shallow", "intermediate", "deep"], "gt_class": ["shallow"]}, "meta_data": {"trace_name": "bucket3$791,:3,:15600"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_158", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_158.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_687", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_687.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_807", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_807.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_63", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_63.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1064", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1064.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_141", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_141.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_26", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_26.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_231", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_231.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1200", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1200.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1194", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1194.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_930", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_930.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_937", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_937.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_890", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_890.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_662", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_662.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_852", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_852.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_328", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_328.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_705", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_705.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_56", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_56.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_841", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_841.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_771", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_771.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_243", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_243.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1040", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1040.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_5", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_5.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1291", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1291.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_668", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_668.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_734", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_734.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_979", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_979.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_481", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_481.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1134", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1134.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_468", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_468.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_353", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_353.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_8", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_8.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_114", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_114.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_134", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_134.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_794", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_794.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_679", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_679.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1078", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1078.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1131", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1131.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_518", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_518.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1111", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1111.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_470", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_470.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_830", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_830.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_164", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_164.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1014", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1014.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_203", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_203.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1390", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1390.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1013", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1013.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_45", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_45.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_244", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_244.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_437", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_437.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1254", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1254.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1459", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1459.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_310", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_310.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1306", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1306.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_616", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_616.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_83", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_83.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_159", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_159.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1047", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1047.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_860", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_860.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_80", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_80.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_487", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_487.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1298", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1298.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_73", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_73.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_243", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_243.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1314", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1314.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_248", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_248.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_967", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_967.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1088", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1088.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_191", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_191.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_242", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_242.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_522", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_522.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_733", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_733.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_848", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_848.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_946", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_946.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_124", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_124.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1001", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1001.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_257", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_257.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_79", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_79.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_583", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_583.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_754", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_754.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_40", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_40.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_245", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_245.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_750", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_750.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_641", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_641.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1283", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1283.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_419", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_419.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_426", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_426.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_211", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_211.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_330", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_330.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_988", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_988.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1339", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1339.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_635", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_635.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_85", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_85.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_922", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_922.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_905", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_905.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_19", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_19.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_322", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_322.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_906", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_906.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_887", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_887.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1478", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1478.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_281", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_281.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1059", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1059.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1218", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1218.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_332", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_332.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_13", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_13.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_844", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_844.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_235", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_235.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_247", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_247.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_159", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_159.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_171", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_171.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_45", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_45.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1068", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1068.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_585", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_585.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1274", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1274.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_424", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_424.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_32", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_32.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_130", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_130.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_703", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_703.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_449", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_449.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_254", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_254.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1106", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1106.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_792", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_792.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1483", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1483.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_164", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_164.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_841", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_841.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_742", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_742.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_76", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_76.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_376", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_376.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_116", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_116.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1104", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1104.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1063", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1063.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1105", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1105.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1254", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1254.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_369", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_369.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_833", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_833.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_152", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_152.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_407", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_407.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_749", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_749.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_199", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_199.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1210", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1210.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_627", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_627.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_450", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_450.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_72", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_72.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_251", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_251.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1116", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1116.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_110", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_110.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_877", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_877.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_924", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_924.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_959", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_959.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_108", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_108.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_407", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_407.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1238", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1238.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_338", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_338.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_994", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_994.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_734", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_734.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1306", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1306.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_117", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_117.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_579", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_579.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1175", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1175.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1163", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1163.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_152", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_152.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1299", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1299.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_98", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_98.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_475", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_475.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_167", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_167.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1154", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1154.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_162", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_162.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1012", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1012.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_318", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_318.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_992", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_992.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1074", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1074.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_114", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_114.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_260", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_260.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_50", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_50.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_581", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_581.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1448", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1448.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_292", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_292.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_22", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_22.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_614", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_614.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_441", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_441.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_0", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_0.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_240", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_240.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_421", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_421.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_62", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_62.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1226", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1226.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1023", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1023.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_41", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_41.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_940", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_940.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_416", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_416.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_25", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_25.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_463", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_463.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_241", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_241.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_476", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_476.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_345", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_345.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_291", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_291.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1450", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1450.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_88", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_88.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_172", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_172.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_447", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_447.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1454", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1454.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_513", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_513.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_689", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_689.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_314", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_314.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_432", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_432.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1174", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1174.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_900", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_900.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_923", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_923.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_635", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_635.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_955", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_955.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_582", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_582.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_697", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_697.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_206", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_206.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_81", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_81.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_889", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_889.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_366", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_366.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1260", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1260.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_627", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_627.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_146", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_146.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_562", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_562.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_739", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_739.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_178", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_178.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_791", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_791.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_308", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_308.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_75", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_75.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1133", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1133.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_590", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_590.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_594", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_594.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_596", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_596.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_287", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_287.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_756", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_756.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_13", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_13.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_441", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_441.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_12", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_12.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_227", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_227.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_471", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_471.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1316", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1316.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_985", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_985.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_279", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_279.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_211", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_211.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_444", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_444.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1262", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1262.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_349", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_349.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_217", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_217.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_220", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_220.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_152", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_152.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_939", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_939.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_851", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_851.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1371", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1371.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1430", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1430.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_211", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_211.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_643", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_643.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1159", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1159.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1285", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1285.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_677", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_677.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1352", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1352.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_264", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_264.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1395", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1395.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_851", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_851.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_886", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_886.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_390", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_390.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_163", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_163.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1083", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1083.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_316", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_316.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_815", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_815.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1027", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1027.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1208", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1208.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_377", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_377.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_205", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_205.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_798", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_798.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_504", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_504.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_762", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_762.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_697", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_697.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_329", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_329.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1228", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1228.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_258", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_258.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_324", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_324.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_49", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_49.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_938", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_938.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_37", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_37.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1341", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1341.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_289", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_289.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_880", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_880.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1017", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1017.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_105", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_105.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_650", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_650.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_313", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_313.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1033", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1033.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_184", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_184.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1199", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1199.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_168", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_168.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1273", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1273.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_259", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_259.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1245", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1245.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1449", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1449.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1307", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1307.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_859", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_859.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1418", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1418.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_439", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_439.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_35", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_35.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_277", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_277.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_278", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_278.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_692", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_692.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1021", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1021.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_223", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_223.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_170", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_170.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_838", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_838.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_53", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_53.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_312", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_312.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1095", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1095.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_473", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_473.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1291", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1291.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1238", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1238.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1034", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1034.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_272", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_272.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_133", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_133.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_336", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_336.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_765", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_765.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_295", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_295.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_773", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_773.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1176", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1176.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_8", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_8.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_76", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_76.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_496", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_496.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_124", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_124.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_155", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_155.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_111", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_111.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1004", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1004.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_707", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_707.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_728", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_728.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_406", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_406.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_925", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_925.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1438", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1438.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_250", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_250.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_216", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_216.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_135", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_135.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_432", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_432.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1030", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1030.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1201", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1201.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_447", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_447.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_63", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_63.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_497", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_497.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_132", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_132.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_268", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_268.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1089", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1089.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_234", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_234.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "vt_208", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/vt_208.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Volcano-tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Volcano-tectonic"]}, "meta_data": {"ori_class": "VT"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tr_82", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tr_82.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tremor.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tremor"]}, "meta_data": {"ori_class": "TR"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1326", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1326.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_62", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_62.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_71", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_71.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_873", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_873.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_294", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_294.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_358", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_358.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_648", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_648.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_1284", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_1284.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "tc_1347", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/tc_1347.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Tectonic.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Tectonic"]}, "meta_data": {"ori_class": "TC"}}
{"task_name": "Llaima", "dataset_name": "Llaima", "id": "lp_360", "data_type": "npy", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Earth_Science-Llaima-Volcanic_Seismic_Event_Classification/raw_input/lp_360.npy", "length": 6000, "timestamps": [], "fs": 100}, "input_text": "Given a seismic signal recording from the Llaima dataset, which contains volcanic seismic events collected from the LAV station of the Llaima volcano in Chile, classify the signal into one of the following 4 categories: Volcano-tectonic, Tremor, Tectonic, Long-period. Analyze the seismic signal and determine which category it belongs to.", "gt_text": "The seismic event is classified as Long-period.", "gt_result": {"class_list": ["Long-period", "Tectonic", "Tremor", "Volcano-tectonic"], "gt_class": ["Long-period"]}, "meta_data": {"ori_class": "LP"}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_188", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_188.wav", "length": 19211, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.40 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_048.wav", "length": 21141, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.64 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_059", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_059.wav", "length": 21431, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.68 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_103", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_103.wav", "length": 17746, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.22 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_183", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_183.wav", "length": 20143, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.52 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_118", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_118.wav", "length": 17897, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.24 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_071", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_071.wav", "length": 21517, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_167", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_167.wav", "length": 24123, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.02 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_118", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_118.wav", "length": 20919, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_044", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_044.wav", "length": 20884, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_200", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_200.wav", "length": 19242, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.41 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_022", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_022.wav", "length": 16703, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_013", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_013.wav", "length": 21047, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_133", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_133.wav", "length": 18979, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.37 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_154", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_154.wav", "length": 26577, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.32 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_054.wav", "length": 21026, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_079", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_079.wav", "length": 17460, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.18 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_070", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_070.wav", "length": 20468, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.56 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_048.wav", "length": 27417, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.43 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_145", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_145.wav", "length": 17575, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_026", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_026.wav", "length": 20730, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.59 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_075.wav", "length": 16628, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.08 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_038", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_038.wav", "length": 21493, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_012.wav", "length": 20674, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.58 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_119", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_119.wav", "length": 20990, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.62 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_134", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_134.wav", "length": 24056, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_003", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_003.wav", "length": 23576, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.95 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_123.wav", "length": 17680, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_145", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_145.wav", "length": 18560, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.32 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_097", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_097.wav", "length": 20973, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.62 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_139", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_139.wav", "length": 18577, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.32 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_026", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_026.wav", "length": 20797, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_148", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_148.wav", "length": 23115, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.89 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_085", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_085.wav", "length": 20524, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.57 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_176", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_176.wav", "length": 17584, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_058", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_058.wav", "length": 17798, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.22 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_113", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_113.wav", "length": 17685, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_122", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_122.wav", "length": 20344, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.54 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_020", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_020.wav", "length": 13855, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.73 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_163", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_163.wav", "length": 16329, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.04 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_152", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_152.wav", "length": 15922, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_006.wav", "length": 13750, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.72 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_181", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_181.wav", "length": 20143, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.52 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_153.wav", "length": 26367, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.30 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_154", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_154.wav", "length": 16140, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.02 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_094", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_094.wav", "length": 21669, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.71 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_144.wav", "length": 16091, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_049", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_049.wav", "length": 21481, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_146", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_146.wav", "length": 19171, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.40 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_143", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_143.wav", "length": 23890, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_092", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_092.wav", "length": 21934, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.74 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_176", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_176.wav", "length": 24067, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_137", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_137.wav", "length": 19281, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.41 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_091", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_091.wav", "length": 19394, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.42 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_019", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_019.wav", "length": 20933, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.62 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_070", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_070.wav", "length": 16411, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.05 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_095", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_095.wav", "length": 23539, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.94 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_165.wav", "length": 16806, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.10 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_095", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_095.wav", "length": 20424, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.55 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_121", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_121.wav", "length": 23442, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.93 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_001", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_001.wav", "length": 22311, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.79 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_010", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_010.wav", "length": 16744, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_190", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_190.wav", "length": 16626, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.08 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_126.wav", "length": 23797, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.97 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_116", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_116.wav", "length": 20774, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_189", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_189.wav", "length": 21362, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.67 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_160", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_160.wav", "length": 23058, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.88 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_171", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_171.wav", "length": 17420, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.18 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_044", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_044.wav", "length": 16088, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_109", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_109.wav", "length": 19796, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.47 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_117.wav", "length": 19903, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.49 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_076", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_076.wav", "length": 23370, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.92 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_049", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_049.wav", "length": 23420, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.93 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_065", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_065.wav", "length": 23688, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.96 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_106", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_106.wav", "length": 19346, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.42 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_060.wav", "length": 17576, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_102", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_102.wav", "length": 20654, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.58 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_179", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_179.wav", "length": 16669, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.08 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_166", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_166.wav", "length": 23894, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_101", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_101.wav", "length": 19419, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.43 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_127", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_127.wav", "length": 18842, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.36 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_110", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_110.wav", "length": 19729, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.47 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_125", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_125.wav", "length": 16409, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.05 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_161", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_161.wav", "length": 16694, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_003", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_003.wav", "length": 16933, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.12 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_129.wav", "length": 23928, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_144.wav", "length": 22707, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.84 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_102", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_102.wav", "length": 15507, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.94 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_178", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_178.wav", "length": 17675, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_060.wav", "length": 23392, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.92 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_107", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_107.wav", "length": 20757, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.59 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_091", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_091.wav", "length": 21502, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_135.wav", "length": 17258, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.16 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_059", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_059.wav", "length": 23421, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.93 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_163", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_163.wav", "length": 17123, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.14 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_130", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_130.wav", "length": 17707, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_019", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_019.wav", "length": 16734, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_195", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_195.wav", "length": 19680, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.46 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_157", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_157.wav", "length": 18839, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.35 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_015.wav", "length": 23211, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.90 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_008", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_008.wav", "length": 16874, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.11 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_029", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_029.wav", "length": 16480, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.06 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_097", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_097.wav", "length": 19201, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.40 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_158", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_158.wav", "length": 23289, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.91 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_011", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_011.wav", "length": 19571, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.45 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_052", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_052.wav", "length": 18121, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.27 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_095", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_095.wav", "length": 21042, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_105.wav", "length": 21041, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_194", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_194.wav", "length": 15027, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.88 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_125", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_125.wav", "length": 21012, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_162.wav", "length": 19038, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.38 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_029", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_029.wav", "length": 17592, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_009.wav", "length": 20898, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_115", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_115.wav", "length": 21119, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.64 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_187", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_187.wav", "length": 18970, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.37 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_100", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_100.wav", "length": 24064, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_057.wav", "length": 19135, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.39 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_104", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_104.wav", "length": 20832, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_164", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_164.wav", "length": 23124, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.89 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_033.wav", "length": 16884, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.11 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_003", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_003.wav", "length": 16914, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.11 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_078.wav", "length": 17834, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.23 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_106", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_106.wav", "length": 21041, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.63 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_088", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_088.wav", "length": 24277, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.03 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_181", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_181.wav", "length": 20296, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.54 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_132.wav", "length": 23003, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.88 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_084.wav", "length": 20896, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_010", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_010.wav", "length": 20899, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_112", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_112.wav", "length": 20496, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.56 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_078.wav", "length": 20209, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.53 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_160", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_160.wav", "length": 18974, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.37 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_030.wav", "length": 15978, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.00 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_193", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_193.wav", "length": 14326, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.79 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_094", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_094.wav", "length": 24045, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_145", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_145.wav", "length": 23945, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_173", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_173.wav", "length": 17832, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.23 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_034", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_034.wav", "length": 21611, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.70 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_133", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_133.wav", "length": 16305, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.04 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_180.wav", "length": 16930, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.12 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_185", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_185.wav", "length": 17585, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.20 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_144.wav", "length": 23986, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.00 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_109", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_109.wav", "length": 19356, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.42 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_164", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_164.wav", "length": 16931, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.12 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_138.wav", "length": 23124, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.89 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_179", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_179.wav", "length": 23759, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.97 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_172", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_172.wav", "length": 19324, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.42 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_104", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_104.wav", "length": 17811, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.23 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_137", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_137.wav", "length": 23968, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.00 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_028", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_028.wav", "length": 17653, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.21 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_149", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_149.wav", "length": 23948, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_133", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_133.wav", "length": 23886, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.99 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_025", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_025.wav", "length": 16824, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.10 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_152", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_152.wav", "length": 23289, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.91 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_010", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_010.wav", "length": 13327, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.67 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_072.wav", "length": 23335, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.92 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_198", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_198.wav", "length": 16539, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.07 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_096.wav", "length": 20839, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_191", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_191.wav", "length": 16848, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.11 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_150.wav", "length": 17966, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.25 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_036.wav", "length": 20897, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_149", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_149.wav", "length": 18910, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.36 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_013", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_013.wav", "length": 19513, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.44 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_111.wav", "length": 19284, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.41 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_170", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_170.wav", "length": 24046, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 3.01 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_154", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_154.wav", "length": 23751, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.97 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_072.wav", "length": 20306, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.54 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_122", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_122.wav", "length": 23501, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.94 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_165.wav", "length": 23826, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.98 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_085", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_085.wav", "length": 21450, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.68 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_139", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_139.wav", "length": 18895, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.36 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_130", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_130.wav", "length": 19213, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.40 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_046", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_046.wav", "length": 21533, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_043", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_043.wav", "length": 21246, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.66 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_169", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_169.wav", "length": 23301, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.91 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_106", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_106.wav", "length": 21751, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.72 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_186.wav", "length": 20405, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.55 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_004", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_004.wav", "length": 16696, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_055", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_055.wav", "length": 21537, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.69 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_184", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_184.wav", "length": 16710, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_170", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_170.wav", "length": 22604, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.83 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_044", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_044.wav", "length": 19443, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.43 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_065", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_065.wav", "length": 17365, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.17 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_111.wav", "length": 17811, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.23 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_080", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_080.wav", "length": 21616, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.70 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_074", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_074.wav", "length": 20353, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.54 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_031", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_031.wav", "length": 17459, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.18 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MVP_014", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MVP_014.wav", "length": 20724, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.59 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Valve Prolapse.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Valve Prolapse"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_AS_035", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_AS_035.wav", "length": 20863, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.61 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Aortic Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Aortic Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_014", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_014.wav", "length": 16744, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.09 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_172", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_172.wav", "length": 17003, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.13 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_015.wav", "length": 14805, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 1.85 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_063.wav", "length": 20813, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.60 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_093", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_093.wav", "length": 23495, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.94 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_135.wav", "length": 23824, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.98 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_086", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_086.wav", "length": 16421, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.05 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MS_096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MS_096.wav", "length": 19418, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.43 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Stenosis.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Stenosis"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_023", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_023.wav", "length": 16933, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.12 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_154", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_154.wav", "length": 17446, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.18 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_MR_137", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_MR_137.wav", "length": 17366, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.17 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Mitral Regurgitation.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Mitral Regurgitation"]}, "meta_data": {}}
{"task_name": "Yaseen", "dataset_name": "Yaseen", "id": "New_N_118", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-Yaseen-Heart_Sound_Classification/raw_input/New_N_118.wav", "length": 20374, "timestamps": [], "fs": 8000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 8000 Hz, and a duration of 2.55 seconds. Based on this PCG signal, which of the following categories best describes the heart sound pattern: Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, or Normal?", "gt_text": "The heart sound pattern is recognized as Normal.", "gt_result": {"class_list": ["Aortic Stenosis", "Mitral Regurgitation", "Mitral Stenosis", "Mitral Valve Prolapse", "Normal"], "gt_class": ["Normal"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "23625_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/23625_AV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "23625_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/23625_PV.wav", "length": 122560, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.64 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "23625_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/23625_TV.wav", "length": 81984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "23625_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/23625_MV.wav", "length": 85120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "24160_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/24160_AV.wav", "length": 51584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "24160_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/24160_PV.wav", "length": 46720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "24160_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/24160_TV.wav", "length": 56384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "24160_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/24160_MV.wav", "length": 90368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "46579_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/46579_AV.wav", "length": 78208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "46579_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/46579_PV.wav", "length": 85568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "46579_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/46579_TV.wav", "length": 62976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "46579_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/46579_MV.wav", "length": 85568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49618_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49618_AV.wav", "length": 84352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49618_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49618_MV.wav", "length": 148352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49628_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49628_AV.wav", "length": 126592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49628_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49628_PV.wav", "length": 118208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49628_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49628_TV.wav", "length": 121600, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.40 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49628_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49628_MV.wav", "length": 123584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49630_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49630_AV.wav", "length": 116608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49630_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49630_PV.wav", "length": 102784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49630_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49630_TV.wav", "length": 126336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49630_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49630_MV.wav", "length": 185984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 46.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49631_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49631_AV.wav", "length": 144768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 36.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49631_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49631_MV.wav", "length": 120000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49638_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49638_AV.wav", "length": 166784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 41.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49638_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49638_PV.wav", "length": 100224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49638_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49638_MV.wav", "length": 85824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49687_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49687_AV.wav", "length": 117376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49687_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49687_PV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49687_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49687_TV.wav", "length": 123968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49687_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49687_MV.wav", "length": 135552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49704_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49704_AV.wav", "length": 127168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49704_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49704_MV.wav", "length": 126592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49754_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49754_AV.wav", "length": 152384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49754_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49754_PV.wav", "length": 141184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49754_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49754_TV.wav", "length": 141376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49754_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49754_MV.wav", "length": 143424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49761_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49761_AV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49761_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49761_MV.wav", "length": 125568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49808_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49808_PV.wav", "length": 77824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49808_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49808_TV.wav", "length": 58624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49808_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49808_MV.wav", "length": 62208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49832_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49832_AV.wav", "length": 125568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49832_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49832_MV.wav", "length": 123776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49838_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49838_MV.wav", "length": 111552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49842_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49842_AV.wav", "length": 127552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49842_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49842_PV.wav", "length": 103552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49842_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49842_TV.wav", "length": 107968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49842_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49842_MV.wav", "length": 103040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49897_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49897_AV.wav", "length": 121344, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49897_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49897_PV.wav", "length": 124352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49897_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49897_TV.wav", "length": 124224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49897_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49897_MV.wav", "length": 124992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49931_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49931_AV.wav", "length": 127040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49931_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49931_PV.wav", "length": 126976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49931_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49931_TV.wav", "length": 131840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49968_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49968_PV.wav", "length": 71808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49968_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49968_TV.wav", "length": 71360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49968_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49968_MV.wav", "length": 87168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49978_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49978_PV.wav", "length": 59136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49986_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49986_AV.wav", "length": 102336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49986_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49986_MV.wav", "length": 68224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49989_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49989_PV.wav", "length": 30208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "49989_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/49989_MV.wav", "length": 57920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50012_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50012_MV.wav", "length": 87040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50023_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50023_AV.wav", "length": 118656, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50023_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50023_PV.wav", "length": 122624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50023_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50023_TV.wav", "length": 120768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50023_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50023_MV.wav", "length": 125376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50026_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50026_AV.wav", "length": 136384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50026_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50026_MV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50030_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50030_PV.wav", "length": 70080, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.52 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50030_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50030_TV.wav", "length": 61184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50030_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50030_MV.wav", "length": 87040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50043_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50043_AV.wav", "length": 108608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50043_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50043_PV.wav", "length": 98368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50043_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50043_TV.wav", "length": 153984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50048_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50048_PV.wav", "length": 129407, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50048_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50048_TV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50048_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50048_MV.wav", "length": 199936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 49.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50066_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50066_AV.wav", "length": 121920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50066_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50066_MV.wav", "length": 136640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50067_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50067_PV.wav", "length": 122240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50067_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50067_TV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50067_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50067_MV.wav", "length": 124736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50085_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50085_AV.wav", "length": 63360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50085_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50085_PV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50085_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50085_TV.wav", "length": 78976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50085_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50085_MV.wav", "length": 91840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50100_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50100_AV.wav", "length": 131392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50100_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50100_MV.wav", "length": 155136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50116_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50116_AV.wav", "length": 111808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50116_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50116_PV.wav", "length": 111808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50116_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50116_TV.wav", "length": 113600, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.40 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50116_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50116_MV.wav", "length": 115968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50126_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50126_AV.wav", "length": 115904, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50126_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50126_PV.wav", "length": 83584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50126_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50126_TV.wav", "length": 97536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50126_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50126_MV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50137_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50137_PV.wav", "length": 110720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50137_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50137_TV.wav", "length": 115200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50137_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50137_MV.wav", "length": 117184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50138_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50138_AV.wav", "length": 121408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50138_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50138_MV.wav", "length": 123584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50164_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50164_PV.wav", "length": 119936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50164_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50164_TV.wav", "length": 134016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50164_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50164_MV.wav", "length": 123456, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50204_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50204_AV.wav", "length": 123968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50204_MV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50204_MV_1.wav", "length": 67584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50204_MV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50204_MV_2.wav", "length": 148992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50206_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50206_PV.wav", "length": 91136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50206_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50206_TV.wav", "length": 81920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50206_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50206_MV.wav", "length": 77568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50217_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50217_AV.wav", "length": 124992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50217_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50217_PV.wav", "length": 127168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50218_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50218_AV.wav", "length": 91840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50218_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50218_PV.wav", "length": 66752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50218_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50218_TV.wav", "length": 67392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50218_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50218_MV.wav", "length": 96192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50219_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50219_AV.wav", "length": 140352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50219_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50219_PV.wav", "length": 64384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50219_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50219_MV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50225_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50225_AV.wav", "length": 131392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50225_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50225_PV.wav", "length": 112640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50225_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50225_TV.wav", "length": 153216, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50225_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50225_MV.wav", "length": 125184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50230_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50230_AV.wav", "length": 117568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50230_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50230_PV.wav", "length": 110784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50230_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50230_TV.wav", "length": 118208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50230_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50230_MV.wav", "length": 161536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 40.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50244_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50244_AV.wav", "length": 134592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50244_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50244_PV.wav", "length": 118784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50244_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50244_TV.wav", "length": 125376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50244_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50244_MV.wav", "length": 127552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50247_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50247_AV.wav", "length": 108608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50247_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50247_PV.wav", "length": 125184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50247_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50247_MV.wav", "length": 134208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50248_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50248_TV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50249_AV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50249_AV_1.wav", "length": 62016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50249_AV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50249_AV_2.wav", "length": 74240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50249_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50249_MV.wav", "length": 121792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_AV.wav", "length": 108224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_PV.wav", "length": 115968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_TV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_TV_1.wav", "length": 79808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_TV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_TV_2.wav", "length": 90752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50250_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50250_MV.wav", "length": 117952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50278_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50278_AV.wav", "length": 110016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50278_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50278_PV.wav", "length": 115200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50278_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50278_TV.wav", "length": 107008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50278_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50278_MV.wav", "length": 129151, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50280_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50280_AV.wav", "length": 100032, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.01 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50280_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50280_PV.wav", "length": 111808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50280_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50280_TV.wav", "length": 149056, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50289_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50289_AV.wav", "length": 113408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50289_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50289_PV.wav", "length": 102720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50289_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50289_TV.wav", "length": 120640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50289_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50289_MV.wav", "length": 118976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50291_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50291_AV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50291_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50291_PV.wav", "length": 111616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50291_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50291_MV.wav", "length": 71168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50295_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50295_AV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50295_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50295_PV.wav", "length": 125440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50295_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50295_TV.wav", "length": 157568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 39.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50295_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50295_MV.wav", "length": 153536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 38.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50300_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50300_AV.wav", "length": 115520, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.88 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50300_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50300_MV.wav", "length": 111936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50321_AV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50321_AV_1.wav", "length": 210752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 52.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50321_AV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50321_AV_2.wav", "length": 123392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50321_MV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50321_MV_1.wav", "length": 20992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 5.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50321_MV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50321_MV_2.wav", "length": 142208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50323_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50323_AV.wav", "length": 123968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50323_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50323_PV.wav", "length": 149440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50323_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50323_TV.wav", "length": 121920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50323_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50323_MV.wav", "length": 162816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 40.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50326_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50326_AV.wav", "length": 110784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50326_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50326_PV.wav", "length": 111552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50326_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50326_TV.wav", "length": 110976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50326_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50326_MV.wav", "length": 114816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50335_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50335_AV.wav", "length": 117824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50335_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50335_MV.wav", "length": 123840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50341_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50341_AV.wav", "length": 89792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50341_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50341_MV.wav", "length": 135168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50386_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50386_AV.wav", "length": 147840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 36.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50386_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50386_PV.wav", "length": 123136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50386_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50386_TV.wav", "length": 122560, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.64 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50386_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50386_MV.wav", "length": 122368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50640_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50640_MV.wav", "length": 129151, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50644_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50644_AV.wav", "length": 119808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50644_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50644_MV.wav", "length": 121600, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.40 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50646_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50646_AV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50646_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50646_MV.wav", "length": 134784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50649_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50649_AV.wav", "length": 119424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50649_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50649_PV.wav", "length": 120960, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.24 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50649_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50649_TV.wav", "length": 120640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50649_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50649_MV.wav", "length": 123392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50652_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50652_AV.wav", "length": 118400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50652_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50652_MV.wav", "length": 122368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50655_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50655_AV.wav", "length": 118784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50655_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50655_MV.wav", "length": 139200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50667_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50667_AV.wav", "length": 123392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50667_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50667_PV.wav", "length": 122752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50667_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50667_TV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50667_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50667_MV.wav", "length": 123456, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50669_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50669_AV.wav", "length": 108992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50669_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50669_PV.wav", "length": 116992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50669_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50669_TV.wav", "length": 116608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50669_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50669_MV.wav", "length": 124352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50680_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50680_AV.wav", "length": 123584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50680_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50680_PV.wav", "length": 123776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50680_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50680_TV.wav", "length": 117440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50680_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50680_MV.wav", "length": 118720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50688_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50688_AV.wav", "length": 117184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50688_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50688_PV.wav", "length": 117824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50688_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50688_TV.wav", "length": 115584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50688_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50688_MV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50689_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50689_AV.wav", "length": 124160, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.04 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50689_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50689_PV.wav", "length": 122368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50689_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50689_TV.wav", "length": 115840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50689_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50689_MV.wav", "length": 123776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_AV.wav", "length": 121024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_PV.wav", "length": 124800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_TV.wav", "length": 123200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_MV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_MV_1.wav", "length": 35584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50690_MV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50690_MV_2.wav", "length": 121408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50691_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50691_AV.wav", "length": 116352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50691_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50691_PV.wav", "length": 131008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50691_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50691_TV.wav", "length": 119552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50691_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50691_MV.wav", "length": 130752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50707_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50707_AV.wav", "length": 125760, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.44 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50707_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50707_TV.wav", "length": 121984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50707_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50707_MV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50713_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50713_AV.wav", "length": 120000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50713_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50713_PV.wav", "length": 118720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50713_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50713_TV.wav", "length": 118016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50713_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50713_MV.wav", "length": 120192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50720_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50720_AV.wav", "length": 128768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50720_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50720_MV.wav", "length": 143552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 35.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50723_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50723_AV.wav", "length": 124608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50723_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50723_PV.wav", "length": 122752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50723_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50723_TV.wav", "length": 117184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50723_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50723_MV.wav", "length": 121920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50725_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50725_AV.wav", "length": 157760, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 39.44 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50725_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50725_PV.wav", "length": 150976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 37.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50725_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50725_TV.wav", "length": 126592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50725_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50725_MV.wav", "length": 130368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50735_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50735_AV.wav", "length": 92608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50735_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50735_PV.wav", "length": 89920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50735_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50735_TV.wav", "length": 97408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50735_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50735_MV.wav", "length": 116608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50743_AV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50743_AV_1.wav", "length": 106944, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50743_AV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50743_AV_2.wav", "length": 27072, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 6.77 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50743_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50743_MV.wav", "length": 124352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50746_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50746_AV.wav", "length": 116352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50746_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50746_PV.wav", "length": 125568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50746_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50746_TV.wav", "length": 118592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50746_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50746_MV.wav", "length": 115584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50752_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50752_AV.wav", "length": 113984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50752_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50752_PV.wav", "length": 109376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50752_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50752_TV.wav", "length": 101376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50752_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50752_MV.wav", "length": 113792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50766_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50766_AV.wav", "length": 120768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50766_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50766_PV.wav", "length": 122176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50766_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50766_TV.wav", "length": 124608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50766_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50766_MV.wav", "length": 128000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50774_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50774_AV.wav", "length": 121024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50774_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50774_TV.wav", "length": 117440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50774_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50774_MV.wav", "length": 122816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50781_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50781_AV.wav", "length": 116224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50781_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50781_PV.wav", "length": 117568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50781_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50781_TV.wav", "length": 118592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50781_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50781_MV.wav", "length": 119616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50788_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50788_AV.wav", "length": 116992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50788_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50788_PV.wav", "length": 123200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50788_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50788_TV.wav", "length": 119040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50788_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50788_MV.wav", "length": 121152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50790_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50790_AV.wav", "length": 112384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50790_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50790_MV.wav", "length": 116416, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_AV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_PV.wav", "length": 116800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_TV_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_TV_1.wav", "length": 37632, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.41 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_TV_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_TV_2.wav", "length": 116160, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.04 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50802_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50802_MV.wav", "length": 119552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50807_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50807_AV.wav", "length": 119168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50807_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50807_PV.wav", "length": 124352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50807_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50807_TV.wav", "length": 117824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50807_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50807_MV.wav", "length": 125952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50820_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50820_AV.wav", "length": 121856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50820_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50820_PV.wav", "length": 117184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50820_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50820_TV.wav", "length": 187968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 46.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "50820_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/50820_MV.wav", "length": 119936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "57706_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/57706_AV.wav", "length": 82368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "57706_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/57706_PV.wav", "length": 78400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "57706_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/57706_TV.wav", "length": 76992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "57706_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/57706_MV.wav", "length": 88192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "59536_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/59536_AV.wav", "length": 93184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "59536_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/59536_MV.wav", "length": 126592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "63581_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/63581_AV.wav", "length": 76224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "63581_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/63581_PV.wav", "length": 94400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "63581_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/63581_TV.wav", "length": 78976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "63581_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/63581_MV.wav", "length": 76992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68194_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68194_AV.wav", "length": 36992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68194_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68194_PV.wav", "length": 49152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68194_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68194_TV.wav", "length": 32000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68194_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68194_MV.wav", "length": 46656, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68303_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68303_AV.wav", "length": 50752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68303_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68303_PV.wav", "length": 55360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68303_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68303_TV.wav", "length": 35008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68303_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68303_MV.wav", "length": 45184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68359_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68359_AV.wav", "length": 36608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68359_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68359_PV.wav", "length": 66752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68359_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68359_TV.wav", "length": 41984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68359_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68359_MV.wav", "length": 47808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68377_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68377_AV.wav", "length": 42368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68377_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68377_PV.wav", "length": 58368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68377_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68377_TV.wav", "length": 52352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68377_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68377_MV.wav", "length": 64768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68379_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68379_AV.wav", "length": 42368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68379_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68379_PV.wav", "length": 52800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68379_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68379_TV.wav", "length": 44224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68379_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68379_MV.wav", "length": 59968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68395_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68395_PV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68395_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68395_TV.wav", "length": 85376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68407_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68407_AV.wav", "length": 58176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68407_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68407_PV.wav", "length": 64768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68407_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68407_TV.wav", "length": 60736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68407_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68407_MV.wav", "length": 60736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68413_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68413_AV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68413_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68413_PV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68413_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68413_TV.wav", "length": 83200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68413_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68413_MV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68427_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68427_AV.wav", "length": 64384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68427_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68427_PV.wav", "length": 75392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68427_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68427_TV.wav", "length": 60608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68427_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68427_MV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68460_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68460_AV.wav", "length": 86720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68460_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68460_PV.wav", "length": 69184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68460_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68460_TV.wav", "length": 84736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68460_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68460_MV.wav", "length": 102720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68482_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68482_AV.wav", "length": 46784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68482_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68482_PV.wav", "length": 54784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68482_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68482_TV.wav", "length": 43200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68482_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68482_MV.wav", "length": 70336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68484_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68484_AV.wav", "length": 55040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68484_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68484_PV.wav", "length": 34752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68484_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68484_TV.wav", "length": 32768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68484_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68484_MV.wav", "length": 53440, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.36 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68487_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68487_AV.wav", "length": 84736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68487_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68487_PV.wav", "length": 88384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68487_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68487_TV.wav", "length": 127040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68487_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68487_MV.wav", "length": 91136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68556_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68556_AV.wav", "length": 56256, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68556_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68556_MV.wav", "length": 37824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68560_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68560_AV.wav", "length": 52224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68560_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68560_PV.wav", "length": 60224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68560_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68560_TV.wav", "length": 48768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68560_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68560_MV.wav", "length": 137216, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68864_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68864_PV.wav", "length": 72192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68864_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68864_TV.wav", "length": 70336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68864_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68864_MV.wav", "length": 105536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68888_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68888_AV.wav", "length": 45824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68888_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68888_PV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68888_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68888_TV.wav", "length": 63936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "68888_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/68888_MV.wav", "length": 64191, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69067_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69067_AV.wav", "length": 56576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69067_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69067_PV.wav", "length": 46592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69067_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69067_TV.wav", "length": 49792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69067_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69067_MV.wav", "length": 71616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69093_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69093_AV.wav", "length": 84608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69093_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69093_PV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69093_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69093_TV.wav", "length": 116992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69093_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69093_MV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69144_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69144_AV.wav", "length": 105152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 26.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69144_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69144_PV.wav", "length": 99136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69144_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69144_TV.wav", "length": 98752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69144_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69144_MV.wav", "length": 84608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69147_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69147_AV.wav", "length": 37824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69147_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69147_PV.wav", "length": 69376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69147_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69147_TV.wav", "length": 48384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69147_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69147_MV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69159_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69159_AV.wav", "length": 48576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69159_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69159_PV.wav", "length": 52352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69159_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69159_TV.wav", "length": 52736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "69159_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/69159_MV.wav", "length": 99968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "72283_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/72283_PV.wav", "length": 84608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "72283_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/72283_TV.wav", "length": 145152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 36.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "72283_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/72283_MV.wav", "length": 82368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "78582_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/78582_AV.wav", "length": 88768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "78582_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/78582_MV.wav", "length": 84352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "81638_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/81638_AV.wav", "length": 82624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "81638_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/81638_MV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84689_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84689_AV.wav", "length": 40384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84689_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84689_PV.wav", "length": 55808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84689_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84689_TV.wav", "length": 60160, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.04 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84689_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84689_MV.wav", "length": 50560, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.64 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84695_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84695_PV.wav", "length": 85184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84695_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84695_MV.wav", "length": 86784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84702_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84702_PV.wav", "length": 59968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84702_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84702_TV.wav", "length": 53952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84702_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84702_MV.wav", "length": 68992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84709_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84709_AV.wav", "length": 63168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84709_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84709_PV.wav", "length": 58368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84709_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84709_TV.wav", "length": 57536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84709_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84709_MV.wav", "length": 60800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84711_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84711_AV.wav", "length": 62336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84711_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84711_PV.wav", "length": 41408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84711_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84711_TV.wav", "length": 63616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84711_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84711_MV.wav", "length": 64768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84713_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84713_AV.wav", "length": 96320, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.08 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84713_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84713_PV.wav", "length": 87552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84713_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84713_TV.wav", "length": 119616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84721_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84721_AV.wav", "length": 62720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84721_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84721_PV.wav", "length": 59456, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84721_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84721_TV.wav", "length": 57984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84721_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84721_MV.wav", "length": 60352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84732_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84732_AV.wav", "length": 81024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84732_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84732_MV.wav", "length": 79424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84738_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84738_AV.wav", "length": 59584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84738_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84738_PV.wav", "length": 65792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84738_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84738_TV.wav", "length": 56576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84738_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84738_MV.wav", "length": 63168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84749_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84749_AV.wav", "length": 87424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84749_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84749_PV.wav", "length": 88768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84749_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84749_TV.wav", "length": 86592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84749_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84749_MV.wav", "length": 90368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84764_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84764_AV.wav", "length": 51584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84764_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84764_PV.wav", "length": 45376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84764_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84764_TV.wav", "length": 41152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84764_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84764_MV.wav", "length": 51968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84765_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84765_AV.wav", "length": 83392, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.85 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84765_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84765_PV.wav", "length": 81024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84765_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84765_TV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84765_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84765_MV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84768_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84768_PV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84768_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84768_TV.wav", "length": 82368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84775_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84775_AV.wav", "length": 60800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84775_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84775_PV.wav", "length": 68736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84775_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84775_TV.wav", "length": 72384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84775_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84775_MV.wav", "length": 43968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84778_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84778_AV.wav", "length": 63552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84778_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84778_PV.wav", "length": 53760, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.44 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84778_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84778_TV.wav", "length": 50240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84778_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84778_MV.wav", "length": 54208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84780_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84780_AV.wav", "length": 65536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84780_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84780_PV.wav", "length": 51968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84780_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84780_TV.wav", "length": 57600, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.40 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84780_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84780_MV.wav", "length": 71808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84785_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84785_AV.wav", "length": 51136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84785_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84785_PV.wav", "length": 46784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84785_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84785_TV.wav", "length": 51840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84785_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84785_MV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84796_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84796_AV.wav", "length": 92608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84796_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84796_MV.wav", "length": 87424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84807_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84807_AV.wav", "length": 79616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84807_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84807_MV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84823_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84823_AV.wav", "length": 44992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84823_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84823_PV.wav", "length": 54400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84823_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84823_TV.wav", "length": 52416, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84823_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84823_MV.wav", "length": 69568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84824_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84824_AV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84824_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84824_MV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84826_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84826_AV.wav", "length": 75008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84826_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84826_PV.wav", "length": 74752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84826_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84826_TV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84826_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84826_MV.wav", "length": 79936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84834_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84834_AV.wav", "length": 74624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84834_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84834_PV.wav", "length": 78208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84834_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84834_TV.wav", "length": 75008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84834_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84834_MV.wav", "length": 76800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84887_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84887_AV.wav", "length": 79360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84887_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84887_PV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84887_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84887_TV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84887_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84887_MV.wav", "length": 71168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84893_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84893_AV.wav", "length": 89152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84893_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84893_PV.wav", "length": 92352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84912_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84912_AV.wav", "length": 84800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84912_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84912_PV.wav", "length": 82816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84912_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84912_TV.wav", "length": 90624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84912_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84912_MV.wav", "length": 83584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84923_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84923_AV.wav", "length": 30720, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.68 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84923_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84923_PV.wav", "length": 36800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84923_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84923_TV.wav", "length": 35968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84923_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84923_MV.wav", "length": 81408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84934_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84934_AV.wav", "length": 121536, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 30.38 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84939_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84939_AV.wav", "length": 88576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84939_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84939_PV.wav", "length": 145792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 36.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84939_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84939_TV.wav", "length": 77952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84939_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84939_MV.wav", "length": 82176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84945_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84945_AV.wav", "length": 84800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84945_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84945_PV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84945_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84945_TV.wav", "length": 42816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84945_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84945_MV.wav", "length": 73408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84947_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84947_AV.wav", "length": 43584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84947_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84947_PV.wav", "length": 43968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84947_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84947_TV.wav", "length": 30784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84947_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84947_MV.wav", "length": 48000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84960_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84960_AV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84960_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84960_PV.wav", "length": 96192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84960_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84960_TV.wav", "length": 127424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84960_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84960_MV.wav", "length": 101952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84969_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84969_MV.wav", "length": 95808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84970_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84970_AV.wav", "length": 91968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84970_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84970_MV.wav", "length": 76736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84976_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84976_AV.wav", "length": 44224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84976_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84976_PV.wav", "length": 40768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84976_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84976_TV.wav", "length": 40768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "84976_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/84976_MV.wav", "length": 95168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85010_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85010_AV.wav", "length": 83840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85010_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85010_PV.wav", "length": 85568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85010_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85010_TV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85010_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85010_MV.wav", "length": 85184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85011_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85011_AV.wav", "length": 82240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85011_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85011_MV.wav", "length": 59968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85012_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85012_AV.wav", "length": 37120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85012_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85012_PV.wav", "length": 32256, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85012_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85012_TV.wav", "length": 37952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85012_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85012_MV.wav", "length": 44736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85020_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85020_AV.wav", "length": 138176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85020_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85020_PV.wav", "length": 86400, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.60 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85020_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85020_TV.wav", "length": 139584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 34.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85020_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85020_MV.wav", "length": 79424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85024_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85024_AV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85024_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85024_PV.wav", "length": 82240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85024_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85024_TV.wav", "length": 80640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85024_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85024_MV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85035_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85035_AV.wav", "length": 48576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85035_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85035_PV.wav", "length": 36992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85035_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85035_TV.wav", "length": 32000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85035_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85035_MV.wav", "length": 92736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85037_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85037_AV.wav", "length": 58624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85037_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85037_PV.wav", "length": 96384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85037_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85037_TV.wav", "length": 46208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85037_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85037_MV.wav", "length": 87936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85053_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85053_AV.wav", "length": 98176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85053_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85053_PV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85053_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85053_TV.wav", "length": 115584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85053_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85053_MV.wav", "length": 64575, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85055_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85055_AV.wav", "length": 99840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85055_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85055_PV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85055_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85055_TV.wav", "length": 108608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85069_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85069_AV.wav", "length": 29568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85069_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85069_PV.wav", "length": 30208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85069_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85069_TV.wav", "length": 45376, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.34 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85069_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85069_MV.wav", "length": 52608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85077_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85077_AV.wav", "length": 83840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85077_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85077_PV.wav", "length": 86976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85077_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85077_TV.wav", "length": 88768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85077_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85077_MV.wav", "length": 85056, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85086_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85086_AV.wav", "length": 90752, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.69 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85086_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85086_PV.wav", "length": 80960, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.24 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85086_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85086_TV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85086_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85086_MV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85087_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85087_AV.wav", "length": 34176, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.54 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85087_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85087_PV.wav", "length": 41984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 10.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85087_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85087_TV.wav", "length": 31552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 7.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85087_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85087_MV.wav", "length": 71424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85090_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85090_PV.wav", "length": 82368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85090_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85090_TV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85090_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85090_MV.wav", "length": 79808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85093_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85093_AV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85093_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85093_PV.wav", "length": 95168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85093_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85093_TV.wav", "length": 79808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85093_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85093_MV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85100_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85100_AV.wav", "length": 86272, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.57 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85100_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85100_PV.wav", "length": 91456, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85115_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85115_AV.wav", "length": 35968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 8.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85115_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85115_PV.wav", "length": 62784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85115_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85115_TV.wav", "length": 63552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85115_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85115_MV.wav", "length": 77568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85122_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85122_AV.wav", "length": 95808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85122_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85122_PV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85122_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85122_TV.wav", "length": 110976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85122_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85122_MV.wav", "length": 82240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85133_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85133_AV.wav", "length": 44736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85133_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85133_PV.wav", "length": 53824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 13.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85133_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85133_TV.wav", "length": 61952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85133_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85133_MV.wav", "length": 101824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85140_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85140_AV.wav", "length": 45184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85140_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85140_PV.wav", "length": 63808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85140_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85140_MV.wav", "length": 44608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85150_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85150_AV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85150_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85150_PV.wav", "length": 112256, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85152_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85152_AV.wav", "length": 77824, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85152_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85152_PV.wav", "length": 81856, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.46 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85152_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85152_TV.wav", "length": 80576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85152_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85152_MV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85157_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85157_AV.wav", "length": 39552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 9.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85157_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85157_PV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85157_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85157_TV.wav", "length": 84352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85157_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85157_MV.wav", "length": 115840, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 28.96 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85165_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85165_AV.wav", "length": 77952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85165_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85165_MV.wav", "length": 98240, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.56 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85166_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85166_AV.wav", "length": 84352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85166_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85166_PV.wav", "length": 100224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85166_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85166_TV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85166_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85166_MV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85169_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85169_AV.wav", "length": 78592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85169_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85169_PV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85169_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85169_TV.wav", "length": 79424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85169_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85169_MV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85174_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85174_AV.wav", "length": 94784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85174_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85174_PV.wav", "length": 100352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85174_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85174_TV.wav", "length": 135168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85174_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85174_MV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85175_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85175_AV.wav", "length": 78336, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.58 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85175_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85175_PV.wav", "length": 87552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85175_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85175_TV.wav", "length": 83136, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.78 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85175_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85175_MV.wav", "length": 77184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85176_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85176_AV.wav", "length": 58816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85176_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85176_PV.wav", "length": 64000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85176_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85176_TV.wav", "length": 59008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85176_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85176_MV.wav", "length": 131584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85199_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85199_AV.wav", "length": 93568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85199_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85199_PV.wav", "length": 81792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85212_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85212_AV.wav", "length": 80640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85212_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85212_PV.wav", "length": 80192, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.05 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85212_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85212_TV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85212_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85212_MV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85226_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85226_PV.wav", "length": 68992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85226_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85226_TV.wav", "length": 51776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85226_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85226_MV.wav", "length": 85568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85227_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85227_AV.wav", "length": 128000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85227_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85227_PV.wav", "length": 88000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85227_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85227_TV.wav", "length": 69952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85227_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85227_MV.wav", "length": 60992, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 15.25 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85230_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85230_AV.wav", "length": 77120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85230_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85230_PV.wav", "length": 81408, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.35 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85230_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85230_TV.wav", "length": 91968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85230_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85230_MV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85242_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85242_MV.wav", "length": 71936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85243_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85243_AV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85243_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85243_PV.wav", "length": 81024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85246_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85246_AV.wav", "length": 79040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85246_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85246_PV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85246_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85246_TV.wav", "length": 80576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85246_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85246_MV.wav", "length": 79040, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.76 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85249_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85249_AV.wav", "length": 86016, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85249_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85249_PV.wav", "length": 99968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85249_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85249_TV.wav", "length": 119424, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 29.86 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85249_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85249_MV.wav", "length": 90368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85258_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85258_AV.wav", "length": 91584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85258_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85258_PV.wav", "length": 51584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85258_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85258_TV.wav", "length": 76352, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.09 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85258_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85258_MV.wav", "length": 80768, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.19 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85261_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85261_AV.wav", "length": 72576, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.14 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85261_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85261_PV.wav", "length": 51968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85261_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85261_TV.wav", "length": 51968, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.99 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85261_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85261_MV.wav", "length": 95616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85262_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85262_AV.wav", "length": 75200, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.80 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85262_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85262_PV.wav", "length": 108736, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.18 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85262_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85262_TV.wav", "length": 71936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85262_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85262_MV.wav", "length": 134784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 33.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85264_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85264_MV.wav", "length": 93184, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.30 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85278_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85278_AV.wav", "length": 51584, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 12.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85278_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85278_PV.wav", "length": 58368, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 14.59 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85278_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85278_TV.wav", "length": 71552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 17.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85278_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85278_MV.wav", "length": 101120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 25.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85279_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85279_AV.wav", "length": 89792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85279_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85279_PV.wav", "length": 79808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85279_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85279_TV.wav", "length": 125568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 31.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85279_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85279_MV.wav", "length": 97920, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 24.48 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85286_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85286_AV.wav", "length": 80640, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.16 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85286_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85286_PV.wav", "length": 90624, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85286_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85286_TV.wav", "length": 64000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 16.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85286_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85286_MV.wav", "length": 79616, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.90 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85305_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85305_AV.wav", "length": 81152, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.29 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85305_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85305_PV.wav", "length": 79168, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.79 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85305_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85305_TV.wav", "length": 129984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 32.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85305_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85305_MV.wav", "length": 86208, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.55 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85306_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85306_AV.wav", "length": 85056, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85306_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85306_MV.wav", "length": 81792, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.45 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85308_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85308_AV.wav", "length": 79360, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.84 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85308_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85308_PV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85308_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85308_TV.wav", "length": 85120, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.28 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85308_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85308_MV.wav", "length": 92224, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.06 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85313_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85313_PV.wav", "length": 86784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85313_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85313_TV.wav", "length": 108416, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85313_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85313_MV.wav", "length": 76608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85321_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85321_PV.wav", "length": 91776, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 22.94 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85321_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85321_TV.wav", "length": 80384, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.10 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85322_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85322_TV.wav", "length": 44800, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 11.20 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85338_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85338_AV.wav", "length": 92608, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 23.15 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85338_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85338_PV.wav", "length": 79552, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.89 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85338_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85338_TV.wav", "length": 83008, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.75 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85338_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85338_MV.wav", "length": 77568, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.39 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85339_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85339_MV.wav", "length": 80000, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.00 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85340_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85340_AV.wav", "length": 77056, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85340_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85340_PV.wav", "length": 85952, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 21.49 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85340_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85340_TV.wav", "length": 78976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85340_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85340_MV.wav", "length": 81024, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.26 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85341_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85341_AV.wav", "length": 78784, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85341_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85341_PV.wav", "length": 81984, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 20.50 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85341_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85341_TV.wav", "length": 78656, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.66 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85341_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85341_MV.wav", "length": 79936, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.98 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85343_AV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85343_AV.wav", "length": 74816, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 18.70 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as aortic valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["aortic valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85343_PV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85343_PV.wav", "length": 111808, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 27.95 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as pulmonary valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["pulmonary valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85343_TV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85343_TV.wav", "length": 78976, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.74 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as tricuspid valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["tricuspid valve"]}, "meta_data": {}}
{"task_name": "CirCor_loc", "dataset_name": "CirCor", "id": "85343_MV", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Healthcare-CirCor_loc-Auscultation_Location_Identification/raw_input/85343_MV.wav", "length": 78592, "timestamps": [], "fs": 4000}, "input_text": "This is a phonocardiogram (PCG) recording of heart sounds, with a single channel, a sampling rate of 4000 Hz, and a duration of 19.65 seconds. Based on this PCG signal, please identify the auscultation location (aortic valve, tricuspid valve, pulmonary valve, or mitral valve).", "gt_text": "This recording is identified as mitral valve.", "gt_result": {"class_list": ["aortic valve", "tricuspid valve", "pulmonary valve", "mitral valve"], "gt_class": ["mitral valve"]}, "meta_data": {}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5541_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5541_test_sub_8.npy", "length": 229, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9179_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9179_test_sub_12.npy", "length": 108, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_677_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_677_test_sub_6.npy", "length": 237, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7402_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7402_test_sub_15.npy", "length": 172, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1923_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1923_test_sub_17.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8072_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8072_test_sub_0.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8557_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8557_test_sub_2.npy", "length": 261, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_134_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_134_test_sub_18.npy", "length": 82, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6201_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6201_test_sub_1.npy", "length": 228, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_580_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_580_test_sub_15.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9067_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9067_test_sub_4.npy", "length": 179, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1199_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1199_test_sub_5.npy", "length": 261, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6155_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6155_test_sub_6.npy", "length": 207, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2272_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2272_test_sub_4.npy", "length": 102, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2817_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2817_test_sub_12.npy", "length": 144, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8733_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8733_test_sub_9.npy", "length": 301, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4584_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4584_test_sub_17.npy", "length": 90, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7911_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7911_test_sub_6.npy", "length": 141, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8426_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8426_test_sub_13.npy", "length": 116, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5137_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5137_test_sub_16.npy", "length": 143, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8577_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8577_test_sub_11.npy", "length": 173, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8543_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8543_test_sub_13.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4228_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4228_test_sub_8.npy", "length": 82, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4816_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4816_test_sub_6.npy", "length": 113, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5004_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5004_test_sub_7.npy", "length": 187, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9473_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9473_test_sub_7.npy", "length": 172, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7473_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7473_test_sub_18.npy", "length": 42, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4323_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4323_test_sub_0.npy", "length": 33, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2112_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2112_test_sub_2.npy", "length": 110, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3586_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3586_test_sub_3.npy", "length": 151, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2056_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2056_test_sub_14.npy", "length": 195, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4127_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4127_test_sub_1.npy", "length": 258, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2224_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2224_test_sub_2.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4240_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4240_test_sub_1.npy", "length": 49, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3939_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3939_test_sub_0.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5736_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5736_test_sub_6.npy", "length": 142, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1709_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1709_test_sub_2.npy", "length": 200, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3862_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3862_test_sub_5.npy", "length": 250, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4600_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4600_test_sub_11.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6556_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6556_test_sub_0.npy", "length": 307, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7487_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7487_test_sub_9.npy", "length": 56, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5274_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5274_test_sub_8.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_405_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_405_test_sub_3.npy", "length": 190, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_915_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_915_test_sub_0.npy", "length": 236, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9445_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9445_test_sub_2.npy", "length": 275, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9736_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9736_test_sub_10.npy", "length": 105, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1647_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1647_test_sub_2.npy", "length": 73, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9604_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9604_test_sub_11.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6462_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6462_test_sub_2.npy", "length": 33, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5897_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5897_test_sub_4.npy", "length": 250, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5981_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5981_test_sub_5.npy", "length": 67, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1778_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1778_test_sub_11.npy", "length": 229, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4544_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4544_test_sub_2.npy", "length": 238, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7523_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7523_test_sub_2.npy", "length": 40, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2330_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2330_test_sub_12.npy", "length": 246, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7511_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7511_test_sub_12.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4296_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4296_test_sub_10.npy", "length": 37, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1588_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1588_test_sub_15.npy", "length": 121, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2918_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2918_test_sub_17.npy", "length": 141, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6464_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6464_test_sub_0.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_76_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_76_test_sub_2.npy", "length": 308, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_352_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_352_test_sub_11.npy", "length": 146, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9926_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9926_test_sub_18.npy", "length": 178, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1400_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1400_test_sub_18.npy", "length": 293, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8489_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8489_test_sub_3.npy", "length": 243, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1506_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1506_test_sub_4.npy", "length": 168, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7350_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7350_test_sub_20.npy", "length": 214, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9227_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9227_test_sub_2.npy", "length": 92, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9230_test_sub_23", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9230_test_sub_23.npy", "length": 204, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9567_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9567_test_sub_0.npy", "length": 212, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5533_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5533_test_sub_2.npy", "length": 71, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5730_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5730_test_sub_11.npy", "length": 52, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3329_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3329_test_sub_0.npy", "length": 205, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1098_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1098_test_sub_6.npy", "length": 216, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5793_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5793_test_sub_1.npy", "length": 78, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3853_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3853_test_sub_13.npy", "length": 195, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9360_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9360_test_sub_3.npy", "length": 190, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9169_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9169_test_sub_4.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3553_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3553_test_sub_3.npy", "length": 284, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1694_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1694_test_sub_4.npy", "length": 76, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_486_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_486_test_sub_4.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9980_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9980_test_sub_4.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2548_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2548_test_sub_5.npy", "length": 66, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7703_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7703_test_sub_3.npy", "length": 64, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3073_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3073_test_sub_4.npy", "length": 39, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2070_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2070_test_sub_11.npy", "length": 57, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8364_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8364_test_sub_10.npy", "length": 222, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8324_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8324_test_sub_7.npy", "length": 155, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8040_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8040_test_sub_3.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5967_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5967_test_sub_5.npy", "length": 226, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4100_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4100_test_sub_4.npy", "length": 151, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4231_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4231_test_sub_14.npy", "length": 170, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4214_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4214_test_sub_2.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7314_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7314_test_sub_0.npy", "length": 54, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1253_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1253_test_sub_7.npy", "length": 137, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4421_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4421_test_sub_5.npy", "length": 135, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5783_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5783_test_sub_9.npy", "length": 227, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3073_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3073_test_sub_14.npy", "length": 174, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_841_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_841_test_sub_12.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7577_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7577_test_sub_5.npy", "length": 77, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3233_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3233_test_sub_4.npy", "length": 101, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4803_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4803_test_sub_0.npy", "length": 91, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2273_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2273_test_sub_7.npy", "length": 107, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3285_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3285_test_sub_12.npy", "length": 118, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4065_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4065_test_sub_1.npy", "length": 74, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4535_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4535_test_sub_2.npy", "length": 100, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_127_test_sub_24", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_127_test_sub_24.npy", "length": 143, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8198_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8198_test_sub_0.npy", "length": 49, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7550_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7550_test_sub_17.npy", "length": 64, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3356_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3356_test_sub_4.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6441_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6441_test_sub_10.npy", "length": 167, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9742_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9742_test_sub_12.npy", "length": 297, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2159_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2159_test_sub_3.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8788_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8788_test_sub_1.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9842_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9842_test_sub_0.npy", "length": 264, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6562_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6562_test_sub_11.npy", "length": 136, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2642_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2642_test_sub_2.npy", "length": 65, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1970_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1970_test_sub_2.npy", "length": 65, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4979_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4979_test_sub_0.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6209_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6209_test_sub_7.npy", "length": 63, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9967_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9967_test_sub_8.npy", "length": 260, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5520_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5520_test_sub_3.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3952_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3952_test_sub_3.npy", "length": 261, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5701_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5701_test_sub_12.npy", "length": 270, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1160_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1160_test_sub_5.npy", "length": 188, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5211_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5211_test_sub_0.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4765_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4765_test_sub_7.npy", "length": 47, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4676_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4676_test_sub_11.npy", "length": 201, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_112_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_112_test_sub_9.npy", "length": 150, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7432_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7432_test_sub_12.npy", "length": 120, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8461_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8461_test_sub_9.npy", "length": 139, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4744_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4744_test_sub_15.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6667_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6667_test_sub_9.npy", "length": 297, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6857_test_sub_23", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6857_test_sub_23.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8449_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8449_test_sub_10.npy", "length": 252, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9936_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9936_test_sub_18.npy", "length": 220, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1188_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1188_test_sub_20.npy", "length": 68, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5386_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5386_test_sub_8.npy", "length": 240, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1207_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1207_test_sub_1.npy", "length": 241, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2506_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2506_test_sub_8.npy", "length": 52, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9012_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9012_test_sub_18.npy", "length": 75, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8034_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8034_test_sub_5.npy", "length": 255, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9296_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9296_test_sub_2.npy", "length": 66, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2514_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2514_test_sub_13.npy", "length": 100, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1269_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1269_test_sub_15.npy", "length": 310, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9446_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9446_test_sub_1.npy", "length": 312, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2356_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2356_test_sub_5.npy", "length": 51, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3122_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3122_test_sub_3.npy", "length": 276, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1392_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1392_test_sub_5.npy", "length": 277, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7514_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7514_test_sub_0.npy", "length": 59, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1848_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1848_test_sub_3.npy", "length": 199, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9777_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9777_test_sub_0.npy", "length": 302, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7456_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7456_test_sub_8.npy", "length": 44, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_319_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_319_test_sub_7.npy", "length": 284, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2038_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2038_test_sub_10.npy", "length": 106, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3188_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3188_test_sub_5.npy", "length": 199, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1981_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1981_test_sub_2.npy", "length": 57, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7344_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7344_test_sub_8.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9896_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9896_test_sub_9.npy", "length": 189, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7101_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7101_test_sub_10.npy", "length": 193, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5790_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5790_test_sub_17.npy", "length": 67, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2626_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2626_test_sub_8.npy", "length": 242, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6_test_sub_15.npy", "length": 47, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6240_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6240_test_sub_11.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6300_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6300_test_sub_5.npy", "length": 138, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_376_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_376_test_sub_16.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6205_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6205_test_sub_3.npy", "length": 288, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2030_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2030_test_sub_10.npy", "length": 220, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1499_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1499_test_sub_3.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2091_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2091_test_sub_0.npy", "length": 90, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3744_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3744_test_sub_1.npy", "length": 55, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_923_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_923_test_sub_7.npy", "length": 45, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_829_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_829_test_sub_12.npy", "length": 44, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9320_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9320_test_sub_11.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6069_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6069_test_sub_3.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_619_test_sub_25", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_619_test_sub_25.npy", "length": 81, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7412_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7412_test_sub_17.npy", "length": 201, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7677_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7677_test_sub_1.npy", "length": 118, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1395_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1395_test_sub_3.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1730_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1730_test_sub_10.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5010_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5010_test_sub_7.npy", "length": 251, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1384_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1384_test_sub_3.npy", "length": 314, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7702_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7702_test_sub_0.npy", "length": 147, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5096_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5096_test_sub_15.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8183_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8183_test_sub_6.npy", "length": 73, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6420_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6420_test_sub_2.npy", "length": 110, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2070_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2070_test_sub_8.npy", "length": 207, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5693_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5693_test_sub_12.npy", "length": 231, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6633_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6633_test_sub_8.npy", "length": 274, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6006_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6006_test_sub_10.npy", "length": 317, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9229_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9229_test_sub_0.npy", "length": 106, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7426_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7426_test_sub_7.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2823_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2823_test_sub_13.npy", "length": 183, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_750_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_750_test_sub_3.npy", "length": 318, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5602_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5602_test_sub_11.npy", "length": 103, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5791_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5791_test_sub_8.npy", "length": 290, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8543_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8543_test_sub_10.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9284_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9284_test_sub_0.npy", "length": 244, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9319_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9319_test_sub_16.npy", "length": 99, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4412_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4412_test_sub_1.npy", "length": 117, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9541_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9541_test_sub_13.npy", "length": 67, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6552_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6552_test_sub_8.npy", "length": 238, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6410_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6410_test_sub_0.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5056_test_sub_19", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5056_test_sub_19.npy", "length": 109, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1342_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1342_test_sub_6.npy", "length": 172, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1248_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1248_test_sub_7.npy", "length": 273, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_512_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_512_test_sub_17.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3424_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3424_test_sub_5.npy", "length": 236, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1130_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1130_test_sub_2.npy", "length": 170, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2651_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2651_test_sub_11.npy", "length": 81, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8508_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8508_test_sub_5.npy", "length": 86, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8911_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8911_test_sub_7.npy", "length": 221, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8543_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8543_test_sub_16.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_900_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_900_test_sub_7.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1969_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1969_test_sub_17.npy", "length": 251, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9802_test_sub_24", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9802_test_sub_24.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4380_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4380_test_sub_16.npy", "length": 127, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7472_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7472_test_sub_11.npy", "length": 84, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4222_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4222_test_sub_12.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7602_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7602_test_sub_0.npy", "length": 277, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9049_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9049_test_sub_2.npy", "length": 283, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2783_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2783_test_sub_14.npy", "length": 235, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8351_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8351_test_sub_14.npy", "length": 88, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6940_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6940_test_sub_17.npy", "length": 142, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2952_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2952_test_sub_14.npy", "length": 274, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8695_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8695_test_sub_11.npy", "length": 213, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9306_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9306_test_sub_7.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2760_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2760_test_sub_7.npy", "length": 216, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5387_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5387_test_sub_0.npy", "length": 201, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3960_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3960_test_sub_20.npy", "length": 160, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9803_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9803_test_sub_6.npy", "length": 297, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1314_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1314_test_sub_5.npy", "length": 218, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1601_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1601_test_sub_2.npy", "length": 169, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5473_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5473_test_sub_1.npy", "length": 299, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2611_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2611_test_sub_14.npy", "length": 191, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9466_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9466_test_sub_6.npy", "length": 146, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1243_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1243_test_sub_3.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8816_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8816_test_sub_3.npy", "length": 218, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5062_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5062_test_sub_14.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2183_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2183_test_sub_9.npy", "length": 249, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3179_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3179_test_sub_9.npy", "length": 237, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4501_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4501_test_sub_1.npy", "length": 270, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4072_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4072_test_sub_4.npy", "length": 267, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1753_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1753_test_sub_3.npy", "length": 318, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2826_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2826_test_sub_2.npy", "length": 129, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1393_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1393_test_sub_7.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8248_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8248_test_sub_11.npy", "length": 137, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_552_test_sub_29", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_552_test_sub_29.npy", "length": 293, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6054_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6054_test_sub_6.npy", "length": 184, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3312_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3312_test_sub_16.npy", "length": 260, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_938_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_938_test_sub_5.npy", "length": 39, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2369_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2369_test_sub_8.npy", "length": 228, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2046_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2046_test_sub_10.npy", "length": 315, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8985_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8985_test_sub_2.npy", "length": 179, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5671_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5671_test_sub_9.npy", "length": 41, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4219_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4219_test_sub_1.npy", "length": 311, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2343_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2343_test_sub_2.npy", "length": 184, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3543_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3543_test_sub_3.npy", "length": 127, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_311_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_311_test_sub_4.npy", "length": 185, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9792_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9792_test_sub_3.npy", "length": 300, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2273_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2273_test_sub_11.npy", "length": 283, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6154_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6154_test_sub_3.npy", "length": 232, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6594_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6594_test_sub_2.npy", "length": 258, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1471_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1471_test_sub_3.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3115_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3115_test_sub_7.npy", "length": 68, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_831_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_831_test_sub_2.npy", "length": 114, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1571_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1571_test_sub_8.npy", "length": 268, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6551_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6551_test_sub_2.npy", "length": 36, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5687_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5687_test_sub_2.npy", "length": 215, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1287_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1287_test_sub_11.npy", "length": 313, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4883_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4883_test_sub_7.npy", "length": 184, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_20_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_20_test_sub_2.npy", "length": 144, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7269_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7269_test_sub_5.npy", "length": 279, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1546_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1546_test_sub_10.npy", "length": 201, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4700_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4700_test_sub_6.npy", "length": 251, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9597_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9597_test_sub_1.npy", "length": 273, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8170_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8170_test_sub_12.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1637_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1637_test_sub_7.npy", "length": 146, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8280_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8280_test_sub_8.npy", "length": 231, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4043_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4043_test_sub_11.npy", "length": 300, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9985_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9985_test_sub_1.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5438_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5438_test_sub_17.npy", "length": 143, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3029_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3029_test_sub_9.npy", "length": 216, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4634_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4634_test_sub_0.npy", "length": 69, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2670_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2670_test_sub_2.npy", "length": 49, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6397_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6397_test_sub_0.npy", "length": 117, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_956_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_956_test_sub_13.npy", "length": 179, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4742_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4742_test_sub_2.npy", "length": 98, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5317_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5317_test_sub_8.npy", "length": 126, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8564_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8564_test_sub_5.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3166_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3166_test_sub_3.npy", "length": 122, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9285_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9285_test_sub_6.npy", "length": 59, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3197_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3197_test_sub_12.npy", "length": 167, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1265_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1265_test_sub_2.npy", "length": 77, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7395_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7395_test_sub_0.npy", "length": 241, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1070_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1070_test_sub_0.npy", "length": 68, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_803_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_803_test_sub_2.npy", "length": 98, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1506_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1506_test_sub_3.npy", "length": 264, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9690_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9690_test_sub_12.npy", "length": 235, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_106_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_106_test_sub_5.npy", "length": 270, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7068_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7068_test_sub_9.npy", "length": 188, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3613_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3613_test_sub_10.npy", "length": 205, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9027_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9027_test_sub_7.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1214_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1214_test_sub_6.npy", "length": 139, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3740_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3740_test_sub_3.npy", "length": 134, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_993_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_993_test_sub_10.npy", "length": 60, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3287_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3287_test_sub_7.npy", "length": 203, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5470_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5470_test_sub_10.npy", "length": 134, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5367_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5367_test_sub_0.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5303_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5303_test_sub_14.npy", "length": 120, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9654_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9654_test_sub_5.npy", "length": 78, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3831_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3831_test_sub_5.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_296_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_296_test_sub_9.npy", "length": 192, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2844_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2844_test_sub_1.npy", "length": 59, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4302_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4302_test_sub_5.npy", "length": 319, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_386_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_386_test_sub_5.npy", "length": 254, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1240_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1240_test_sub_12.npy", "length": 41, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9130_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9130_test_sub_0.npy", "length": 224, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_289_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_289_test_sub_0.npy", "length": 228, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8717_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8717_test_sub_16.npy", "length": 311, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8617_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8617_test_sub_14.npy", "length": 209, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6869_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6869_test_sub_0.npy", "length": 244, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4264_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4264_test_sub_0.npy", "length": 34, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_367_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_367_test_sub_20.npy", "length": 295, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3275_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3275_test_sub_2.npy", "length": 281, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5802_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5802_test_sub_10.npy", "length": 166, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_405_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_405_test_sub_8.npy", "length": 38, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_603_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_603_test_sub_0.npy", "length": 59, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1008_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1008_test_sub_4.npy", "length": 253, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2273_test_sub_17", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2273_test_sub_17.npy", "length": 283, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_526_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_526_test_sub_9.npy", "length": 301, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6780_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6780_test_sub_13.npy", "length": 192, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6872_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6872_test_sub_16.npy", "length": 38, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1974_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1974_test_sub_14.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5127_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5127_test_sub_0.npy", "length": 291, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7466_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7466_test_sub_8.npy", "length": 131, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5654_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5654_test_sub_16.npy", "length": 114, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3000_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3000_test_sub_18.npy", "length": 245, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4382_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4382_test_sub_0.npy", "length": 96, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9335_test_sub_25", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9335_test_sub_25.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_888_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_888_test_sub_9.npy", "length": 299, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6236_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6236_test_sub_5.npy", "length": 277, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9371_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9371_test_sub_3.npy", "length": 285, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9194_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9194_test_sub_0.npy", "length": 225, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8021_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8021_test_sub_5.npy", "length": 263, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7213_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7213_test_sub_13.npy", "length": 310, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7333_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7333_test_sub_0.npy", "length": 246, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2611_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2611_test_sub_6.npy", "length": 83, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2172_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2172_test_sub_8.npy", "length": 34, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_838_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_838_test_sub_11.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8165_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8165_test_sub_2.npy", "length": 313, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4786_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4786_test_sub_13.npy", "length": 226, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4134_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4134_test_sub_11.npy", "length": 46, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6433_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6433_test_sub_12.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5785_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5785_test_sub_1.npy", "length": 95, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3272_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3272_test_sub_11.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2477_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2477_test_sub_0.npy", "length": 114, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2161_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2161_test_sub_11.npy", "length": 39, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1437_test_sub_26", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1437_test_sub_26.npy", "length": 53, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1555_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1555_test_sub_18.npy", "length": 243, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8208_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8208_test_sub_9.npy", "length": 158, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2070_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2070_test_sub_0.npy", "length": 135, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8364_test_sub_21", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8364_test_sub_21.npy", "length": 60, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_114_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_114_test_sub_20.npy", "length": 234, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7891_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7891_test_sub_10.npy", "length": 96, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9320_test_sub_16", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9320_test_sub_16.npy", "length": 151, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1909_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1909_test_sub_11.npy", "length": 178, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8163_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8163_test_sub_4.npy", "length": 151, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_365_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_365_test_sub_9.npy", "length": 96, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7269_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7269_test_sub_2.npy", "length": 212, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2242_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2242_test_sub_7.npy", "length": 107, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3050_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3050_test_sub_6.npy", "length": 139, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4207_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4207_test_sub_0.npy", "length": 287, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3914_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3914_test_sub_1.npy", "length": 240, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7298_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7298_test_sub_6.npy", "length": 316, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2230_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2230_test_sub_1.npy", "length": 185, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7818_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7818_test_sub_3.npy", "length": 161, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5789_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5789_test_sub_0.npy", "length": 256, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_28_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_28_test_sub_4.npy", "length": 138, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_539_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_539_test_sub_0.npy", "length": 128, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_787_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_787_test_sub_10.npy", "length": 280, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2224_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2224_test_sub_11.npy", "length": 137, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8790_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8790_test_sub_5.npy", "length": 268, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9466_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9466_test_sub_5.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5791_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5791_test_sub_3.npy", "length": 170, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_274_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_274_test_sub_1.npy", "length": 214, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5423_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5423_test_sub_14.npy", "length": 233, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4040_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4040_test_sub_3.npy", "length": 290, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 0 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 0}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8191_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8191_test_sub_7.npy", "length": 104, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1324_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1324_test_sub_5.npy", "length": 304, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9594_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9594_test_sub_12.npy", "length": 83, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5393_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5393_test_sub_4.npy", "length": 305, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5313_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5313_test_sub_6.npy", "length": 187, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9179_test_sub_13", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9179_test_sub_13.npy", "length": 288, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1736_test_sub_36", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1736_test_sub_36.npy", "length": 137, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1114_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1114_test_sub_9.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8685_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8685_test_sub_3.npy", "length": 103, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_188_test_sub_22", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_188_test_sub_22.npy", "length": 36, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5404_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5404_test_sub_8.npy", "length": 192, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2712_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2712_test_sub_3.npy", "length": 119, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6306_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6306_test_sub_6.npy", "length": 309, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4460_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4460_test_sub_2.npy", "length": 227, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8455_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8455_test_sub_12.npy", "length": 298, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5702_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5702_test_sub_3.npy", "length": 126, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8003_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8003_test_sub_8.npy", "length": 298, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -2 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -2}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7484_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7484_test_sub_6.npy", "length": 83, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3095_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3095_test_sub_0.npy", "length": 70, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3083_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3083_test_sub_12.npy", "length": 87, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9073_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9073_test_sub_1.npy", "length": 36, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3173_test_sub_32", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3173_test_sub_32.npy", "length": 271, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4986_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4986_test_sub_3.npy", "length": 50, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3441_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3441_test_sub_1.npy", "length": 60, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2213_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2213_test_sub_3.npy", "length": 104, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1762_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1762_test_sub_4.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3638_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3638_test_sub_2.npy", "length": 171, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -6}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8276_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8276_test_sub_1.npy", "length": 283, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5780_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5780_test_sub_9.npy", "length": 61, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6363_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6363_test_sub_0.npy", "length": 157, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3762_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3762_test_sub_5.npy", "length": 289, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8272_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8272_test_sub_6.npy", "length": 130, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3503_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3503_test_sub_5.npy", "length": 135, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8639_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8639_test_sub_5.npy", "length": 86, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1026_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1026_test_sub_15.npy", "length": 189, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5021_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5021_test_sub_3.npy", "length": 199, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8578_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8578_test_sub_8.npy", "length": 312, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8355_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8355_test_sub_8.npy", "length": 168, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2890_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2890_test_sub_6.npy", "length": 97, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6302_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6302_test_sub_10.npy", "length": 60, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5200_test_sub_11", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5200_test_sub_11.npy", "length": 195, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6791_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6791_test_sub_7.npy", "length": 145, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4082_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4082_test_sub_12.npy", "length": 172, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4312_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4312_test_sub_10.npy", "length": 68, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6931_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6931_test_sub_8.npy", "length": 262, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8315_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8315_test_sub_12.npy", "length": 255, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_915_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_915_test_sub_6.npy", "length": 254, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8676_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8676_test_sub_1.npy", "length": 272, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5209_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5209_test_sub_14.npy", "length": 160, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4829_test_sub_2", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4829_test_sub_2.npy", "length": 272, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3890_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3890_test_sub_7.npy", "length": 79, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2261_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2261_test_sub_15.npy", "length": 206, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -19}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1778_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1778_test_sub_8.npy", "length": 229, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_826_test_sub_9", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_826_test_sub_9.npy", "length": 288, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3181_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3181_test_sub_3.npy", "length": 314, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1462_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1462_test_sub_5.npy", "length": 77, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2233_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2233_test_sub_3.npy", "length": 199, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_630_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_630_test_sub_7.npy", "length": 43, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6536_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6536_test_sub_6.npy", "length": 45, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8475_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8475_test_sub_1.npy", "length": 279, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4925_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4925_test_sub_10.npy", "length": 229, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9075_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9075_test_sub_8.npy", "length": 72, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4624_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4624_test_sub_12.npy", "length": 121, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2089_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2089_test_sub_7.npy", "length": 292, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9189_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9189_test_sub_14.npy", "length": 237, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": -4}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4445_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4445_test_sub_1.npy", "length": 186, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5963_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5963_test_sub_7.npy", "length": 234, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_162_test_sub_10", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_162_test_sub_10.npy", "length": 236, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6114_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6114_test_sub_3.npy", "length": 39, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -5}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4172_test_sub_20", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4172_test_sub_20.npy", "length": 117, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1425_test_sub_6", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1425_test_sub_6.npy", "length": 282, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8688_test_sub_4", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8688_test_sub_4.npy", "length": 209, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7191_test_sub_8", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7191_test_sub_8.npy", "length": 63, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7627_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7627_test_sub_0.npy", "length": 168, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -18}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6680_test_sub_14", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6680_test_sub_14.npy", "length": 157, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 13}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2039_test_sub_1", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2039_test_sub_1.npy", "length": 95, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3637_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3637_test_sub_3.npy", "length": 205, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2084_test_sub_5", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2084_test_sub_5.npy", "length": 254, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_5235_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5235_test_sub_0.npy", "length": 86, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -3}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8205_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8205_test_sub_7.npy", "length": 77, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 11}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2723_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2723_test_sub_3.npy", "length": 135, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 12}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4817_test_sub_3", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4817_test_sub_3.npy", "length": 88, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -7}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4879_test_sub_24", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4879_test_sub_24.npy", "length": 47, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -10}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1954_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1954_test_sub_0.npy", "length": 212, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": -16}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_4449_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4449_test_sub_7.npy", "length": 35, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3191_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3191_test_sub_12.npy", "length": 234, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -8}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_6608_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6608_test_sub_18.npy", "length": 307, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 17}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3192_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3192_test_sub_0.npy", "length": 202, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_7754_test_sub_19", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7754_test_sub_19.npy", "length": 40, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": 15}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_1551_test_sub_7", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1551_test_sub_7.npy", "length": 142, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Coherent Unmodulated Pulse Trains.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Coherent Unmodulated Pulse Trains"]}, "meta_data": {"snr": -14}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_9755_test_sub_0", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9755_test_sub_0.npy", "length": 159, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Barker codes"]}, "meta_data": {"snr": 1}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_3954_test_sub_12", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3954_test_sub_12.npy", "length": 289, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Polyphase Barker codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Polyphase Barker codes"]}, "meta_data": {"snr": 20}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_2848_test_sub_18", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2848_test_sub_18.npy", "length": 166, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Linear Frequency-Modulated (LFM) pulses.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Linear Frequency-Modulated (LFM) pulses"]}, "meta_data": {"snr": 9}}
{"task_name": "RadSeg", "dataset_name": "RadSeg", "id": "RadarSeg_8831_test_sub_15", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["real", "imag"], "path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8831_test_sub_15.npy", "length": 169, "timestamps": [], "fs": 3200000}, "input_text": "Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase Barker codes, Frank codes, Linear Frequency-Modulated (LFM) pulses, or Coherent Unmodulated Pulse Trains. Please determine the category to which this signal segment belongs.", "gt_text": "This signal is classified as Frank codes.", "gt_result": {"class_list": ["Barker codes", "Polyphase Barker codes", "Frank codes", "Linear Frequency-Modulated (LFM) pulses", "Coherent Unmodulated Pulse Trains"], "gt_class": ["Frank codes"]}, "meta_data": {"snr": 16}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000143", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000143.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 143}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000156", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000156.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 156}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-4_000034", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-4_000034.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -4, "sample_index_in_group": 34}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-4_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-4_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -4, "sample_index_in_group": 133}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-10_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-10_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -10, "sample_index_in_group": 140}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-2_000063", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-2_000063.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -2, "sample_index_in_group": 63}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_6_000033", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000033.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 6, "sample_index_in_group": 33}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 79}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_4_000144", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_4_000144.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 4, "sample_index_in_group": 144}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-18_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -18, "sample_index_in_group": 108}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000127", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000127.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 127}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_0_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_0_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 0, "sample_index_in_group": 196}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-8_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-8_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -8, "sample_index_in_group": 76}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 171}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-20_000002", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-20_000002.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -20, "sample_index_in_group": 2}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-12_000033", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-12_000033.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -12, "sample_index_in_group": 33}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-20_000131", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-20_000131.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -20, "sample_index_in_group": 131}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_10_000166", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_10_000166.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 10, "sample_index_in_group": 166}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_12_000197", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_12_000197.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 12, "sample_index_in_group": 197}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_18_000016", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_18_000016.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 18, "sample_index_in_group": 16}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-20_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-20_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -20, "sample_index_in_group": 128}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-6_000163", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-6_000163.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -6, "sample_index_in_group": 163}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-14_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -14, "sample_index_in_group": 26}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_0_000080", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_0_000080.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 0, "sample_index_in_group": 80}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-6_000199", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-6_000199.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -6, "sample_index_in_group": 199}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000032", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000032.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 32}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000174", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000174.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 174}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-20_000038", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-20_000038.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -20, "sample_index_in_group": 38}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-2_000117", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-2_000117.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -2, "sample_index_in_group": 117}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000071", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000071.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 71}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-2_000039", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-2_000039.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -2, "sample_index_in_group": 39}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_4_000077", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_4_000077.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 4, "sample_index_in_group": 77}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000090", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000090.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 90}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-10_000029", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-10_000029.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -10, "sample_index_in_group": 29}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000039", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000039.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 39}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_10_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_10_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 10, "sample_index_in_group": 13}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-14_000061", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000061.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -14, "sample_index_in_group": 61}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-6_000176", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-6_000176.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -6, "sample_index_in_group": 176}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_4_000037", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_4_000037.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 4, "sample_index_in_group": 37}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-4_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-4_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -4, "sample_index_in_group": 128}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_4_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_4_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 4, "sample_index_in_group": 95}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_12_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_12_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 12, "sample_index_in_group": 13}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000152", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000152.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 152}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000074", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000074.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 74}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_0_000021", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_0_000021.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 0, "sample_index_in_group": 21}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000151", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000151.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 151}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-4_000132", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-4_000132.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -4, "sample_index_in_group": 132}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-16_000125", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-16_000125.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -16, "sample_index_in_group": 125}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-14_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-14_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -14, "sample_index_in_group": 81}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_6_000102", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_6_000102.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 6, "sample_index_in_group": 102}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_2_000172", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_2_000172.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 2, "sample_index_in_group": 172}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_18_000120", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_18_000120.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 18, "sample_index_in_group": 120}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_8_000085", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_8_000085.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 8, "sample_index_in_group": 85}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000016", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000016.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 16}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_16_000145", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_16_000145.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 16, "sample_index_in_group": 145}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000122", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000122.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 122}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_4_000174", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_4_000174.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 4, "sample_index_in_group": 174}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-20_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-20_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -20, "sample_index_in_group": 86}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000054", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000054.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 54}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-14_000046", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-14_000046.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -14, "sample_index_in_group": 46}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000016", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000016.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 16}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-6_000131", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-6_000131.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -6, "sample_index_in_group": 131}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_2_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_2_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 2, "sample_index_in_group": 8}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000194", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000194.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 194}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 26}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_14_000127", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_14_000127.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 14, "sample_index_in_group": 127}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-4_000043", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-4_000043.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -4, "sample_index_in_group": 43}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000056", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000056.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 56}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_18_000036", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_18_000036.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 18, "sample_index_in_group": 36}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000129", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000129.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 129}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 92}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_2_000066", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_2_000066.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 2, "sample_index_in_group": 66}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-2_000057", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-2_000057.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -2, "sample_index_in_group": 57}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-12_000091", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-12_000091.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -12, "sample_index_in_group": 91}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_8_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_8_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 8, "sample_index_in_group": 189}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_4_000042", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_4_000042.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 4, "sample_index_in_group": 42}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-8_000153", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-8_000153.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -8, "sample_index_in_group": 153}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_12_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_12_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 12, "sample_index_in_group": 79}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000199", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000199.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 199}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-20_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-20_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -20, "sample_index_in_group": 79}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_18_000045", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_18_000045.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 18, "sample_index_in_group": 45}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_18_000169", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_18_000169.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 18, "sample_index_in_group": 169}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_4_000018", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_4_000018.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 4, "sample_index_in_group": 18}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_6_000192", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000192.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 6, "sample_index_in_group": 192}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_2_000123", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_2_000123.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 2, "sample_index_in_group": 123}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000021", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000021.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 21}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_4_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_4_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 4, "sample_index_in_group": 13}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_4_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_4_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 4, "sample_index_in_group": 72}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-10_000068", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-10_000068.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -10, "sample_index_in_group": 68}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000148", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000148.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 148}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-8_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-8_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -8, "sample_index_in_group": 86}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000141", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000141.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 141}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-8_000193", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-8_000193.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -8, "sample_index_in_group": 193}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_16_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_16_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 16, "sample_index_in_group": 177}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000195", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000195.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 195}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_2_000051", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_2_000051.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 2, "sample_index_in_group": 51}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-16_000024", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-16_000024.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -16, "sample_index_in_group": 24}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_4_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_4_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 4, "sample_index_in_group": 133}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-18_000083", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-18_000083.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -18, "sample_index_in_group": 83}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_10_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_10_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 10, "sample_index_in_group": 79}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_8_000162", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_8_000162.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 8, "sample_index_in_group": 162}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000106", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000106.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 106}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_16_000194", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_16_000194.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 16, "sample_index_in_group": 194}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_6_000061", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_6_000061.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 6, "sample_index_in_group": 61}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-18_000048", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000048.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -18, "sample_index_in_group": 48}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000033", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000033.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 33}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000167", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000167.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 167}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_0_000167", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_0_000167.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 0, "sample_index_in_group": 167}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_4_000094", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_4_000094.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 4, "sample_index_in_group": 94}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000099", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000099.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 99}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 133}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_4_000010", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_4_000010.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 4, "sample_index_in_group": 10}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_12_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_12_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 12, "sample_index_in_group": 105}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-4_000049", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-4_000049.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -4, "sample_index_in_group": 49}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-18_000097", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-18_000097.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -18, "sample_index_in_group": 97}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_0_000141", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_0_000141.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 0, "sample_index_in_group": 141}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-16_000009", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-16_000009.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -16, "sample_index_in_group": 9}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-2_000188", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-2_000188.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -2, "sample_index_in_group": 188}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_16_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_16_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 16, "sample_index_in_group": 108}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-14_000179", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-14_000179.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -14, "sample_index_in_group": 179}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_4_000170", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_4_000170.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 4, "sample_index_in_group": 170}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_18_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_18_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 18, "sample_index_in_group": 177}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 108}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_16_000014", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_16_000014.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 16, "sample_index_in_group": 14}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-18_000100", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-18_000100.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -18, "sample_index_in_group": 100}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-16_000056", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-16_000056.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -16, "sample_index_in_group": 56}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-18_000029", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-18_000029.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -18, "sample_index_in_group": 29}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000120", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000120.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 120}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-8_000088", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-8_000088.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -8, "sample_index_in_group": 88}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000024", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000024.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 24}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-8_000059", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-8_000059.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -8, "sample_index_in_group": 59}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-14_000179", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-14_000179.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -14, "sample_index_in_group": 179}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-14_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-14_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -14, "sample_index_in_group": 76}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-16_000001", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-16_000001.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -16, "sample_index_in_group": 1}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-18_000037", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-18_000037.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -18, "sample_index_in_group": 37}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_18_000005", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_18_000005.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 18, "sample_index_in_group": 5}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 76}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_14_000130", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_14_000130.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 14, "sample_index_in_group": 130}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-16_000058", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-16_000058.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -16, "sample_index_in_group": 58}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_12_000146", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_12_000146.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 12, "sample_index_in_group": 146}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_16_000046", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_16_000046.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 16, "sample_index_in_group": 46}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000181", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000181.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 181}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-12_000062", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-12_000062.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -12, "sample_index_in_group": 62}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_2_000020", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_2_000020.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 2, "sample_index_in_group": 20}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_14_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_14_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 14, "sample_index_in_group": 92}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_6_000078", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_6_000078.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 6, "sample_index_in_group": 78}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-20_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-20_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -20, "sample_index_in_group": 187}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_2_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_2_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 2, "sample_index_in_group": 138}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 115}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-12_000075", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-12_000075.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -12, "sample_index_in_group": 75}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000004", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000004.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 4}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000064", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000064.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 64}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_14_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_14_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 14, "sample_index_in_group": 196}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-10_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-10_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -10, "sample_index_in_group": 8}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 81}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_0_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_0_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 0, "sample_index_in_group": 187}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-10_000078", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-10_000078.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -10, "sample_index_in_group": 78}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-10_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-10_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -10, "sample_index_in_group": 105}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-10_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-10_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -10, "sample_index_in_group": 138}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-14_000100", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-14_000100.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -14, "sample_index_in_group": 100}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_0_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_0_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 0, "sample_index_in_group": 72}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-8_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-8_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -8, "sample_index_in_group": 189}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-10_000023", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-10_000023.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -10, "sample_index_in_group": 23}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_18_000150", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_18_000150.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 18, "sample_index_in_group": 150}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000126", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000126.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 126}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-6_000004", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-6_000004.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -6, "sample_index_in_group": 4}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 138}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_14_000047", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_14_000047.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 14, "sample_index_in_group": 47}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_2_000025", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_2_000025.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 2, "sample_index_in_group": 25}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_6_000151", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_6_000151.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 6, "sample_index_in_group": 151}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_6_000158", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_6_000158.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 6, "sample_index_in_group": 158}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_16_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_16_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 16, "sample_index_in_group": 8}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000103", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000103.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 103}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_14_000165", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_14_000165.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 14, "sample_index_in_group": 165}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-4_000194", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-4_000194.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -4, "sample_index_in_group": 194}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_2_000032", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_2_000032.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 2, "sample_index_in_group": 32}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_16_000001", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_16_000001.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 16, "sample_index_in_group": 1}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000049", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000049.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 49}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-10_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-10_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -10, "sample_index_in_group": 15}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-14_000055", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-14_000055.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -14, "sample_index_in_group": 55}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000014", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000014.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 14}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-2_000136", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-2_000136.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -2, "sample_index_in_group": 136}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-2_000193", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-2_000193.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -2, "sample_index_in_group": 193}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000165", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000165.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 165}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-2_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-2_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -2, "sample_index_in_group": 72}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-16_000091", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-16_000091.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -16, "sample_index_in_group": 91}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-8_000178", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-8_000178.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -8, "sample_index_in_group": 178}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-10_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-10_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -10, "sample_index_in_group": 140}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-10_000018", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-10_000018.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -10, "sample_index_in_group": 18}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000094", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000094.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 94}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-20_000192", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-20_000192.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -20, "sample_index_in_group": 192}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-4_000020", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-4_000020.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -4, "sample_index_in_group": 20}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-8_000193", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-8_000193.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -8, "sample_index_in_group": 193}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000114", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000114.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 114}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_16_000023", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_16_000023.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 16, "sample_index_in_group": 23}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_8_000132", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_8_000132.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 8, "sample_index_in_group": 132}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-14_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-14_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -14, "sample_index_in_group": 187}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000062", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000062.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 62}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-20_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-20_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -20, "sample_index_in_group": 13}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-8_000039", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-8_000039.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -8, "sample_index_in_group": 39}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_2_000160", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_2_000160.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 2, "sample_index_in_group": 160}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-10_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-10_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -10, "sample_index_in_group": 92}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-12_000168", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-12_000168.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -12, "sample_index_in_group": 168}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-4_000117", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-4_000117.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -4, "sample_index_in_group": 117}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000028", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000028.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 28}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-18_000182", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000182.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -18, "sample_index_in_group": 182}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-2_000190", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-2_000190.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -2, "sample_index_in_group": 190}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-8_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-8_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -8, "sample_index_in_group": 95}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000021", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000021.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 21}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-4_000053", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-4_000053.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -4, "sample_index_in_group": 53}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000070", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000070.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 70}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_8_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_8_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 8, "sample_index_in_group": 72}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_4_000009", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_4_000009.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 4, "sample_index_in_group": 9}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_2_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_2_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 2, "sample_index_in_group": 81}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-4_000194", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-4_000194.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -4, "sample_index_in_group": 194}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-20_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-20_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -20, "sample_index_in_group": 177}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000102", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000102.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 102}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-10_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-10_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -10, "sample_index_in_group": 76}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-10_000003", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-10_000003.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -10, "sample_index_in_group": 3}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000034", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000034.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 34}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-16_000018", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-16_000018.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -16, "sample_index_in_group": 18}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_6_000111", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_6_000111.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 6, "sample_index_in_group": 111}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-16_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-16_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -16, "sample_index_in_group": 171}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_18_000060", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_18_000060.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 18, "sample_index_in_group": 60}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_2_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_2_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 2, "sample_index_in_group": 8}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000005", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000005.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 5}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_10_000136", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_10_000136.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 10, "sample_index_in_group": 136}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000130", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000130.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 130}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_0_000166", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_0_000166.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 0, "sample_index_in_group": 166}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 8}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-4_000042", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-4_000042.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -4, "sample_index_in_group": 42}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_8_000192", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_8_000192.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 8, "sample_index_in_group": 192}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_16_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_16_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 16, "sample_index_in_group": 140}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000066", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000066.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 66}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-10_000126", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-10_000126.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -10, "sample_index_in_group": 126}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_8_000139", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_8_000139.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 8, "sample_index_in_group": 139}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 95}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_8_000157", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_8_000157.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 8, "sample_index_in_group": 157}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_0_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_0_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 0, "sample_index_in_group": 196}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_10_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_10_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 10, "sample_index_in_group": 86}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_8_000120", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_8_000120.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 8, "sample_index_in_group": 120}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_14_000125", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_14_000125.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 14, "sample_index_in_group": 125}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-8_000000", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-8_000000.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -8, "sample_index_in_group": 0}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_12_000104", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_12_000104.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 12, "sample_index_in_group": 104}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-20_000029", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-20_000029.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -20, "sample_index_in_group": 29}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_18_000004", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_18_000004.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 18, "sample_index_in_group": 4}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 15}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_14_000006", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_14_000006.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 14, "sample_index_in_group": 6}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_6_000043", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000043.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 6, "sample_index_in_group": 43}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_0_000001", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_0_000001.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 0, "sample_index_in_group": 1}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_6_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_6_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 6, "sample_index_in_group": 108}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_16_000044", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_16_000044.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 16, "sample_index_in_group": 44}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_18_000185", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_18_000185.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 18, "sample_index_in_group": 185}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-16_000142", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-16_000142.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -16, "sample_index_in_group": 142}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-14_000036", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-14_000036.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -14, "sample_index_in_group": 36}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_18_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_18_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 18, "sample_index_in_group": 105}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000122", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000122.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 122}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000120", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000120.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 120}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000083", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000083.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 83}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_10_000040", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_10_000040.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 10, "sample_index_in_group": 40}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000075", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000075.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 75}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_2_000033", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_2_000033.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 2, "sample_index_in_group": 33}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-10_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-10_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -10, "sample_index_in_group": 86}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000172", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000172.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 172}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-12_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-12_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -12, "sample_index_in_group": 133}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 189}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_10_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_10_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 10, "sample_index_in_group": 95}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-8_000184", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-8_000184.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -8, "sample_index_in_group": 184}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-20_000162", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-20_000162.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -20, "sample_index_in_group": 162}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_4_000184", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_4_000184.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 4, "sample_index_in_group": 184}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_0_000059", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_0_000059.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 0, "sample_index_in_group": 59}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-16_000141", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-16_000141.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -16, "sample_index_in_group": 141}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-18_000135", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-18_000135.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -18, "sample_index_in_group": 135}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_14_000199", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_14_000199.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 14, "sample_index_in_group": 199}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000094", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000094.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 94}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_14_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_14_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 14, "sample_index_in_group": 187}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_16_000195", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_16_000195.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 16, "sample_index_in_group": 195}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-14_000036", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-14_000036.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -14, "sample_index_in_group": 36}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_6_000187", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000187.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 6, "sample_index_in_group": 187}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_14_000093", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_14_000093.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 14, "sample_index_in_group": 93}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000001", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000001.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 1}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000074", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000074.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 74}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_6_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_6_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 6, "sample_index_in_group": 171}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_16_000024", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_16_000024.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 16, "sample_index_in_group": 24}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000022", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000022.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 22}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-8_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-8_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -8, "sample_index_in_group": 26}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-4_000118", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-4_000118.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -4, "sample_index_in_group": 118}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-6_000129", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-6_000129.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -6, "sample_index_in_group": 129}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_16_000096", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_16_000096.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 16, "sample_index_in_group": 96}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_4_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_4_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 4, "sample_index_in_group": 84}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 177}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-4_000180", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-4_000180.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -4, "sample_index_in_group": 180}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_6_000046", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_6_000046.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 6, "sample_index_in_group": 46}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000102", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000102.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 102}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000049", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000049.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 49}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_2_000164", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_2_000164.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 2, "sample_index_in_group": 164}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_8_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_8_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 8, "sample_index_in_group": 186}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_16_000190", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_16_000190.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 16, "sample_index_in_group": 190}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 115}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_2_000061", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_2_000061.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 2, "sample_index_in_group": 61}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-14_000053", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-14_000053.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -14, "sample_index_in_group": 53}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_14_000173", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_14_000173.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 14, "sample_index_in_group": 173}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_16_000077", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_16_000077.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 16, "sample_index_in_group": 77}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-4_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-4_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -4, "sample_index_in_group": 128}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_18_000062", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_18_000062.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 18, "sample_index_in_group": 62}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000037", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000037.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 37}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 76}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 108}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_4_000161", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_4_000161.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 4, "sample_index_in_group": 161}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_2_000123", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_2_000123.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 2, "sample_index_in_group": 123}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000099", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000099.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 99}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-6_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-6_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -6, "sample_index_in_group": 86}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_2_000192", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_2_000192.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 2, "sample_index_in_group": 192}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_14_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_14_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 14, "sample_index_in_group": 189}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_10_000147", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_10_000147.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 10, "sample_index_in_group": 147}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_12_000097", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_12_000097.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 12, "sample_index_in_group": 97}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-12_000014", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-12_000014.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -12, "sample_index_in_group": 14}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_12_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_12_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 12, "sample_index_in_group": 15}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-6_000029", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-6_000029.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -6, "sample_index_in_group": 29}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_6_000017", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_6_000017.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 6, "sample_index_in_group": 17}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-4_000064", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-4_000064.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -4, "sample_index_in_group": 64}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_0_000050", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000050.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 0, "sample_index_in_group": 50}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_18_000078", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_18_000078.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 18, "sample_index_in_group": 78}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000137", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000137.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 137}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-18_000007", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000007.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -18, "sample_index_in_group": 7}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-4_000154", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-4_000154.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -4, "sample_index_in_group": 154}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_2_000068", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_2_000068.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 2, "sample_index_in_group": 68}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000023", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000023.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 23}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-2_000146", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-2_000146.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -2, "sample_index_in_group": 146}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_12_000107", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_12_000107.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 12, "sample_index_in_group": 107}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000150", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000150.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 150}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-16_000003", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-16_000003.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -16, "sample_index_in_group": 3}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_8_000188", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_8_000188.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 8, "sample_index_in_group": 188}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-18_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-18_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -18, "sample_index_in_group": 81}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_8_000025", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_8_000025.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 8, "sample_index_in_group": 25}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_4_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_4_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 4, "sample_index_in_group": 105}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000189", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000189.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 189}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_10_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_10_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 10, "sample_index_in_group": 115}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_14_000149", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_14_000149.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 14, "sample_index_in_group": 149}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 186}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_18_000180", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_18_000180.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 18, "sample_index_in_group": 180}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 196}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-20_000102", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-20_000102.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -20, "sample_index_in_group": 102}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-20_000060", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-20_000060.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -20, "sample_index_in_group": 60}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-16_000047", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-16_000047.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -16, "sample_index_in_group": 47}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-8_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-8_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -8, "sample_index_in_group": 128}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000190", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000190.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 190}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000180", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000180.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 180}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_8_000152", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_8_000152.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 8, "sample_index_in_group": 152}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000055", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000055.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 55}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_16_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_16_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 16, "sample_index_in_group": 84}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_6_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_6_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 6, "sample_index_in_group": 140}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_18_000106", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_18_000106.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 18, "sample_index_in_group": 106}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_10_000123", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_10_000123.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 10, "sample_index_in_group": 123}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_14_000160", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_14_000160.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 14, "sample_index_in_group": 160}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000041", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000041.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 41}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-8_000127", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-8_000127.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -8, "sample_index_in_group": 127}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_14_000052", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_14_000052.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 14, "sample_index_in_group": 52}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_4_000065", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_4_000065.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 4, "sample_index_in_group": 65}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-16_000136", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-16_000136.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -16, "sample_index_in_group": 136}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_16_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_16_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 16, "sample_index_in_group": 26}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000145", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000145.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 145}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-2_000093", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-2_000093.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -2, "sample_index_in_group": 93}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_18_000121", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_18_000121.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 18, "sample_index_in_group": 121}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-4_000134", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-4_000134.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -4, "sample_index_in_group": 134}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000018", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000018.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 18}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-12_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-12_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -12, "sample_index_in_group": 81}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-18_000172", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-18_000172.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -18, "sample_index_in_group": 172}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_14_000121", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_14_000121.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 14, "sample_index_in_group": 121}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000077", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000077.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 77}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-2_000077", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-2_000077.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -2, "sample_index_in_group": 77}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_12_000142", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_12_000142.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 12, "sample_index_in_group": 142}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 84}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-4_000131", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-4_000131.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -4, "sample_index_in_group": 131}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-4_000021", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-4_000021.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -4, "sample_index_in_group": 21}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-10_000185", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-10_000185.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -10, "sample_index_in_group": 185}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000000", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000000.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 0}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-20_000032", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-20_000032.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -20, "sample_index_in_group": 32}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 128}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000000", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000000.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 0}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-4_000083", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-4_000083.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -4, "sample_index_in_group": 83}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_6_000071", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_6_000071.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 6, "sample_index_in_group": 71}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-8_000022", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-8_000022.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -8, "sample_index_in_group": 22}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_16_000178", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_16_000178.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 16, "sample_index_in_group": 178}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000190", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000190.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 190}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-2_000137", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-2_000137.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -2, "sample_index_in_group": 137}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_14_000087", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_14_000087.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 14, "sample_index_in_group": 87}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-2_000049", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-2_000049.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -2, "sample_index_in_group": 49}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_18_000167", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_18_000167.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 18, "sample_index_in_group": 167}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-18_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-18_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -18, "sample_index_in_group": 92}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_0_000044", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_0_000044.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 0, "sample_index_in_group": 44}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-10_000010", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-10_000010.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -10, "sample_index_in_group": 10}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-2_000152", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-2_000152.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -2, "sample_index_in_group": 152}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000067", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000067.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 67}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-8_000002", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-8_000002.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -8, "sample_index_in_group": 2}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 171}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_6_000061", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_6_000061.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 6, "sample_index_in_group": 61}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-16_000150", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-16_000150.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -16, "sample_index_in_group": 150}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-12_000153", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-12_000153.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -12, "sample_index_in_group": 153}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_2_000014", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_2_000014.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 2, "sample_index_in_group": 14}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-2_000112", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-2_000112.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -2, "sample_index_in_group": 112}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-20_000047", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-20_000047.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -20, "sample_index_in_group": 47}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_12_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_12_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 12, "sample_index_in_group": 105}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_6_000117", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_6_000117.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 6, "sample_index_in_group": 117}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-10_000087", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-10_000087.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -10, "sample_index_in_group": 87}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-16_000111", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-16_000111.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -16, "sample_index_in_group": 111}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_0_000070", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_0_000070.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 0, "sample_index_in_group": 70}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-16_000038", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-16_000038.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -16, "sample_index_in_group": 38}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_8_000182", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_8_000182.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 8, "sample_index_in_group": 182}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-16_000056", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-16_000056.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -16, "sample_index_in_group": 56}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-12_000098", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-12_000098.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -12, "sample_index_in_group": 98}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-14_000047", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000047.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -14, "sample_index_in_group": 47}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_4_000197", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_4_000197.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 4, "sample_index_in_group": 197}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_6_000135", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_6_000135.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 6, "sample_index_in_group": 135}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-18_000122", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-18_000122.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -18, "sample_index_in_group": 122}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-10_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-10_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -10, "sample_index_in_group": 84}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_10_000063", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_10_000063.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 10, "sample_index_in_group": 63}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-2_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-2_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -2, "sample_index_in_group": 81}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 186}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000080", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000080.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 80}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_0_000105", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_0_000105.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 0, "sample_index_in_group": 105}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_4_000136", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_4_000136.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 4, "sample_index_in_group": 136}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_8_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_8_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 8, "sample_index_in_group": 81}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-12_000036", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-12_000036.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -12, "sample_index_in_group": 36}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-14_000131", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000131.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -14, "sample_index_in_group": 131}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_4_000040", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_4_000040.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 4, "sample_index_in_group": 40}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-12_000058", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-12_000058.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -12, "sample_index_in_group": 58}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-6_000051", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-6_000051.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -6, "sample_index_in_group": 51}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000128", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000128.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 128}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_2_000054", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_2_000054.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 2, "sample_index_in_group": 54}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-14_000151", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-14_000151.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -14, "sample_index_in_group": 151}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_6_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_6_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 6, "sample_index_in_group": 76}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_0_000161", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_0_000161.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 0, "sample_index_in_group": 161}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_8_000053", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_8_000053.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 8, "sample_index_in_group": 53}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_0_000181", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_0_000181.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 0, "sample_index_in_group": 181}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_6_000019", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_6_000019.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 6, "sample_index_in_group": 19}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-8_000028", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-8_000028.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -8, "sample_index_in_group": 28}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_2_000071", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_2_000071.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 2, "sample_index_in_group": 71}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_0_000069", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_0_000069.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 0, "sample_index_in_group": 69}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-16_000072", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-16_000072.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -16, "sample_index_in_group": 72}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-14_000066", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-14_000066.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -14, "sample_index_in_group": 66}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_0_000119", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_0_000119.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 0, "sample_index_in_group": 119}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000069", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000069.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 69}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_4_000035", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_4_000035.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 4, "sample_index_in_group": 35}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_8_000197", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_8_000197.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 8, "sample_index_in_group": 197}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_8_000099", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_8_000099.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 8, "sample_index_in_group": 99}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-16_000035", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-16_000035.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -16, "sample_index_in_group": 35}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_10_000059", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_10_000059.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 10, "sample_index_in_group": 59}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-6_000152", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-6_000152.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -6, "sample_index_in_group": 152}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_14_000196", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_14_000196.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 14, "sample_index_in_group": 196}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-14_000026", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-14_000026.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -14, "sample_index_in_group": 26}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_16_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_16_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 16, "sample_index_in_group": 138}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-12_000142", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-12_000142.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -12, "sample_index_in_group": 142}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-10_000086", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-10_000086.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -10, "sample_index_in_group": 86}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_2_000002", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000002.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 2, "sample_index_in_group": 2}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-10_000096", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-10_000096.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -10, "sample_index_in_group": 96}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-16_000095", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-16_000095.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -16, "sample_index_in_group": 95}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-16_000013", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-16_000013.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -16, "sample_index_in_group": 13}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_8_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_8_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 8, "sample_index_in_group": 15}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000078", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000078.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 78}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-10_000083", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-10_000083.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -10, "sample_index_in_group": 83}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-18_000156", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-18_000156.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -18, "sample_index_in_group": 156}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_0_000087", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_0_000087.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 0, "sample_index_in_group": 87}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-10_000085", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-10_000085.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -10, "sample_index_in_group": 85}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-8_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-8_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -8, "sample_index_in_group": 84}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_14_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_14_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 14, "sample_index_in_group": 138}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_14_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_14_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": 14, "sample_index_in_group": 15}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-18_000150", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-18_000150.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -18, "sample_index_in_group": 150}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_18_000031", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_18_000031.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 18, "sample_index_in_group": 31}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000032", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000032.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 32}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_8_000133", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_8_000133.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 8, "sample_index_in_group": 133}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-2_000171", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-2_000171.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -2, "sample_index_in_group": 171}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-10_000139", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-10_000139.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -10, "sample_index_in_group": 139}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-6_000113", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-6_000113.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -6, "sample_index_in_group": 113}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_8_000140", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_8_000140.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 8, "sample_index_in_group": 140}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000166", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000166.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 166}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_6_000025", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_6_000025.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 6, "sample_index_in_group": 25}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000116", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000116.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 116}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-4_000067", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-4_000067.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -4, "sample_index_in_group": 67}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_16_000054", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_16_000054.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 16, "sample_index_in_group": 54}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_WBFM_-10_000154", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-10_000154.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is WBFM.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["WBFM"]}, "meta_data": {"split": "eval", "mod": "WBFM", "snr": -10, "sample_index_in_group": 154}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-6_000154", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-6_000154.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -6, "sample_index_in_group": 154}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-12_000073", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-12_000073.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -12, "sample_index_in_group": 73}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_12_000011", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_12_000011.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 12, "sample_index_in_group": 11}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000034", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000034.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 34}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_10_000051", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_10_000051.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 10, "sample_index_in_group": 51}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_12_000044", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_12_000044.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 12, "sample_index_in_group": 44}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_2_000009", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_2_000009.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 2, "sample_index_in_group": 9}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_18_000028", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_18_000028.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": 18, "sample_index_in_group": 28}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_8_000142", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_8_000142.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 8, "sample_index_in_group": 142}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-10_000093", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-10_000093.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -10, "sample_index_in_group": 93}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-10_000002", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-10_000002.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -10, "sample_index_in_group": 2}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_16_000162", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_16_000162.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 16, "sample_index_in_group": 162}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_0_000175", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_0_000175.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 0, "sample_index_in_group": 175}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-18_000183", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-18_000183.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -18, "sample_index_in_group": 183}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000161", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000161.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 161}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_6_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 6, "sample_index_in_group": 186}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000070", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000070.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 70}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000129", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000129.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 129}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-20_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-20_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -20, "sample_index_in_group": 8}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000084", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000084.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 84}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-18_000071", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-18_000071.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -18, "sample_index_in_group": 71}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_10_000178", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_10_000178.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 10, "sample_index_in_group": 178}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_12_000008", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_12_000008.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 12, "sample_index_in_group": 8}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_12_000109", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_12_000109.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 12, "sample_index_in_group": 109}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_18_000135", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_18_000135.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 18, "sample_index_in_group": 135}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-4_000165", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-4_000165.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -4, "sample_index_in_group": 165}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-10_000168", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-10_000168.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -10, "sample_index_in_group": 168}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_-2_000058", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-2_000058.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": -2, "sample_index_in_group": 58}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-12_000177", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-12_000177.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -12, "sample_index_in_group": 177}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000040", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000040.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 40}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_-18_000197", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-18_000197.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": -18, "sample_index_in_group": 197}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-18_000060", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-18_000060.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -18, "sample_index_in_group": 60}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_2_000098", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_2_000098.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 2, "sample_index_in_group": 98}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-8_000015", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-8_000015.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -8, "sample_index_in_group": 15}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-4_000129", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-4_000129.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -4, "sample_index_in_group": 129}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-6_000082", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-6_000082.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -6, "sample_index_in_group": 82}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_4_000053", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_4_000053.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 4, "sample_index_in_group": 53}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_2_000007", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_2_000007.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": 2, "sample_index_in_group": 7}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_2_000076", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_2_000076.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 2, "sample_index_in_group": 76}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-14_000184", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-14_000184.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -14, "sample_index_in_group": 184}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_6_000199", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_6_000199.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 6, "sample_index_in_group": 199}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-12_000186", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-12_000186.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -12, "sample_index_in_group": 186}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_CPFSK_8_000147", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_8_000147.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is CPFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["CPFSK"]}, "meta_data": {"split": "eval", "mod": "CPFSK", "snr": 8, "sample_index_in_group": 147}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_-16_000174", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-16_000174.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": -16, "sample_index_in_group": 174}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_12_000100", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_12_000100.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 12, "sample_index_in_group": 100}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_16_000181", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_16_000181.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 16, "sample_index_in_group": 181}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_16_000108", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_16_000108.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 16, "sample_index_in_group": 108}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_8_000059", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_8_000059.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 8, "sample_index_in_group": 59}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_12_000166", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_12_000166.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": 12, "sample_index_in_group": 166}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-14_000044", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-14_000044.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -14, "sample_index_in_group": 44}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-2_000079", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-2_000079.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -2, "sample_index_in_group": 79}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM16_14_000106", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_14_000106.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM16.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM16"]}, "meta_data": {"split": "eval", "mod": "QAM16", "snr": 14, "sample_index_in_group": 106}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_12_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_12_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 12, "sample_index_in_group": 115}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-16_000185", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-16_000185.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -16, "sample_index_in_group": 185}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-6_000081", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000081.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -6, "sample_index_in_group": 81}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-14_000069", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-14_000069.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -14, "sample_index_in_group": 69}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-16_000019", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-16_000019.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -16, "sample_index_in_group": 19}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_16_000165", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_16_000165.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": 16, "sample_index_in_group": 165}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QPSK_-12_000163", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_-12_000163.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QPSK"]}, "meta_data": {"split": "eval", "mod": "QPSK", "snr": -12, "sample_index_in_group": 163}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_BPSK_-4_000035", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-4_000035.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is BPSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["BPSK"]}, "meta_data": {"split": "eval", "mod": "BPSK", "snr": -4, "sample_index_in_group": 35}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_-16_000115", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-16_000115.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": -16, "sample_index_in_group": 115}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-SSB_-8_000092", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-8_000092.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-SSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-SSB"]}, "meta_data": {"split": "eval", "mod": "AM-SSB", "snr": -8, "sample_index_in_group": 92}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_PAM4_6_000099", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_6_000099.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is PAM4.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["PAM4"]}, "meta_data": {"split": "eval", "mod": "PAM4", "snr": 6, "sample_index_in_group": 99}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_10_000037", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000037.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": 10, "sample_index_in_group": 37}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_8PSK_-10_000138", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_-10_000138.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is 8PSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["8PSK"]}, "meta_data": {"split": "eval", "mod": "8PSK", "snr": -10, "sample_index_in_group": 138}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_GFSK_10_000093", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_10_000093.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is GFSK.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["GFSK"]}, "meta_data": {"split": "eval", "mod": "GFSK", "snr": 10, "sample_index_in_group": 93}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_QAM64_-18_000005", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-18_000005.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Analyze this wireless communication signal and determine its modulation class. The possible classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is QAM64.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["QAM64"]}, "meta_data": {"split": "eval", "mod": "QAM64", "snr": -18, "sample_index_in_group": 5}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_6_000003", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_6_000003.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": 6, "sample_index_in_group": 3}}
{"task_name": "RadioML", "dataset_name": "RadioML2016.10A", "id": "RML2016.10A_eval_AM-DSB_-18_000179", "data_type": "npy", "input_ts": {"num_channel": 2, "channel_detail": ["I", "Q"], "path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-18_000179.npy", "length": 128, "timestamps": [], "fs": 1000000}, "input_text": "Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM.", "gt_text": "The modulation type is AM-DSB.", "gt_result": {"class_list": ["8PSK", "AM-DSB", "AM-SSB", "BPSK", "CPFSK", "GFSK", "PAM4", "QAM16", "QAM64", "QPSK", "WBFM"], "gt_class": ["AM-DSB"]}, "meta_data": {"split": "eval", "mod": "AM-DSB", "snr": -18, "sample_index_in_group": 179}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "14__10_07_13_piraCies_Espera_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/14__10_07_13_piraCies_Espera_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "14__10_07_13_piraCies_Espera.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "14__10_07_13_piraCies_Espera_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/14__10_07_13_piraCies_Espera_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "14__10_07_13_piraCies_Espera.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "14__10_07_13_piraCies_Espera_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/14__10_07_13_piraCies_Espera_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "14__10_07_13_piraCies_Espera.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "7__10_07_13_marDeCangas_Espera_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/7__10_07_13_marDeCangas_Espera_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "7__10_07_13_marDeCangas_Espera.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "7__10_07_13_marDeCangas_Espera_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/7__10_07_13_marDeCangas_Espera_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "7__10_07_13_marDeCangas_Espera.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "7__10_07_13_marDeCangas_Espera_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/7__10_07_13_marDeCangas_Espera_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "7__10_07_13_marDeCangas_Espera.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "21__18_07_13_lanchaMotora_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/21__18_07_13_lanchaMotora_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "21__18_07_13_lanchaMotora.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "20__18_07_13_AutopridePrepManiobra_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/20__18_07_13_AutopridePrepManiobra_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "20__18_07_13_AutopridePrepManiobra.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "40__19_07_13_MarDeCangas_Llega_Interf_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/40__19_07_13_MarDeCangas_Llega_Interf_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "40__19_07_13_MarDeCangas_Llega_Interf.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "43__19_07_13_PirataDeCies_Llega_Interf_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/43__19_07_13_PirataDeCies_Llega_Interf_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "43__19_07_13_PirataDeCies_Llega_Interf.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "43__19_07_13_PirataDeCies_Llega_Interf_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/43__19_07_13_PirataDeCies_Llega_Interf_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "43__19_07_13_PirataDeCies_Llega_Interf.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "28__19_07_13_NuevoRiaAldan_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/28__19_07_13_NuevoRiaAldan_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Trawler.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Trawler"]}, "meta_data": {"ori_file_path": "28__19_07_13_NuevoRiaAldan.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "31__19_07_13_RemolcadorArrancaPara_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/31__19_07_13_RemolcadorArrancaPara_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Tugboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Tugboat"]}, "meta_data": {"ori_file_path": "31__19_07_13_RemolcadorArrancaPara.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "31__19_07_13_RemolcadorArrancaPara_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/31__19_07_13_RemolcadorArrancaPara_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Tugboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Tugboat"]}, "meta_data": {"ori_file_path": "31__19_07_13_RemolcadorArrancaPara.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "31__19_07_13_RemolcadorArrancaPara_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/31__19_07_13_RemolcadorArrancaPara_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Tugboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Tugboat"]}, "meta_data": {"ori_file_path": "31__19_07_13_RemolcadorArrancaPara.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "30__19_07_13_practico2_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/30__19_07_13_practico2_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Pilot boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Pilot boat"]}, "meta_data": {"ori_file_path": "30__19_07_13_practico2.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "24__19_07_13_adventureFrenando_duda_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/24__19_07_13_adventureFrenando_duda_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Ocean liner.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Ocean liner"]}, "meta_data": {"ori_file_path": "24__19_07_13_adventureFrenando_duda.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "22__19_07_13_adventure_maniobra_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/22__19_07_13_adventure_maniobra_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Ocean liner.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Ocean liner"]}, "meta_data": {"ori_file_path": "22__19_07_13_adventure_maniobra.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "22__19_07_13_adventure_maniobra_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/22__19_07_13_adventure_maniobra_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Ocean liner.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Ocean liner"]}, "meta_data": {"ori_file_path": "22__19_07_13_adventure_maniobra.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 3}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "35__19_07_13_MarDeOnza_sale_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/35__19_07_13_MarDeOnza_sale_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "35__19_07_13_MarDeOnza_sale.wav", "seg_index": 4}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "54__23_07_13_H3_PirataSalvora_SALE1_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/54__23_07_13_H3_PirataSalvora_SALE1_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "54__23_07_13_H3_PirataSalvora_SALE1.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "54__23_07_13_H3_PirataSalvora_SALE1_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/54__23_07_13_H3_PirataSalvora_SALE1_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "54__23_07_13_H3_PirataSalvora_SALE1.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "54__23_07_13_H3_PirataSalvora_SALE1_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/54__23_07_13_H3_PirataSalvora_SALE1_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "54__23_07_13_H3_PirataSalvora_SALE1.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "56__23_07_13_H3_velero1_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/56__23_07_13_H3_velero1_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Sailboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Sailboat"]}, "meta_data": {"ori_file_path": "56__23_07_13_H3_velero1.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "56__23_07_13_H3_velero1_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/56__23_07_13_H3_velero1_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Sailboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Sailboat"]}, "meta_data": {"ori_file_path": "56__23_07_13_H3_velero1.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 3}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "51__23_07_13_H3_lancha2_interf_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/51__23_07_13_H3_lancha2_interf_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Motorboat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Motorboat"]}, "meta_data": {"ori_file_path": "51__23_07_13_H3_lancha2_interf.wav", "seg_index": 4}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 3}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 4}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg5", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg5.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 5}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg6", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg6.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 6}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg7", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg7.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 7}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "66__23_07_13_H3_Pesquerito2_Velero_seg8", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/66__23_07_13_H3_Pesquerito2_Velero_seg8.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Mussel boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Mussel boat"]}, "meta_data": {"ori_file_path": "66__23_07_13_H3_Pesquerito2_Velero.wav", "seg_index": 8}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 3}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 4}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg5", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg5.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 5}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg6", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg6.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 6}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "61__23_07_13_H3_MarDeCangas_SALE-2_seg7", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/61__23_07_13_H3_MarDeCangas_SALE-2_seg7.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Passenger ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Passenger ship"]}, "meta_data": {"ori_file_path": "61__23_07_13_H3_MarDeCangas_SALE-2.wav", "seg_index": 7}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "75__23_07_13_H3_pesquero1_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/75__23_07_13_H3_pesquero1_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Fish boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Fish boat"]}, "meta_data": {"ori_file_path": "75__23_07_13_H3_pesquero1.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "75__23_07_13_H3_pesquero1_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/75__23_07_13_H3_pesquero1_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Fish boat.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Fish boat"]}, "meta_data": {"ori_file_path": "75__23_07_13_H3_pesquero1.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 3}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 4}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg5", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg5.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 5}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg6", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg6.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 6}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg7", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg7.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 7}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg8", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg8.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 8}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg9", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg9.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 9}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "78__23_07_13_H3_vikingChance_seg10", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/78__23_07_13_H3_vikingChance_seg10.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as RORO ship.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["RORO ship"]}, "meta_data": {"ori_file_path": "78__23_07_13_H3_vikingChance.wav", "seg_index": 10}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "85__E__1L_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/85__E__1L_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "85__E__1L.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "85__E__1L_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/85__E__1L_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "85__E__1L.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "85__E__1L_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/85__E__1L_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "85__E__1L.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "85__E__1L_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/85__E__1L_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "85__E__1L.wav", "seg_index": 3}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 3}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "88__E__4L_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/88__E__4L_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "88__E__4L.wav", "seg_index": 4}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 2}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg3.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 3}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg4", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg4.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 4}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg5", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg5.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 5}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "81__25_09_13_H3_corriente_seg6", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/81__25_09_13_H3_corriente_seg6.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Natural ambient noise.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Natural ambient noise"]}, "meta_data": {"ori_file_path": "81__25_09_13_H3_corriente.wav", "seg_index": 6}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "93__A__Draga_1_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/93__A__Draga_1_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Dredger.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Dredger"]}, "meta_data": {"ori_file_path": "93__A__Draga_1.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "95__A__Draga_3_seg0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/95__A__Draga_3_seg0.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Dredger.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Dredger"]}, "meta_data": {"ori_file_path": "95__A__Draga_3.wav", "seg_index": 0}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "95__A__Draga_3_seg1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/95__A__Draga_3_seg1.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Dredger.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Dredger"]}, "meta_data": {"ori_file_path": "95__A__Draga_3.wav", "seg_index": 1}}
{"task_name": "ShipsEar", "dataset_name": "ShipsEar", "id": "95__A__Draga_3_seg2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification/raw_input/95__A__Draga_3_seg2.wav", "length": 1582020, "timestamps": [], "fs": 52734}, "input_text": "Given an underwater acoustic recording from the ShipsEar dataset, which is an underwater vessel noise database collected in real-world environments, classify the vessel or sound source into one of the following 12 categories: Fish boat, Trawler, Mussel boat, Tugboat, Dredger, Motorboat, Pilot boat, Sailboat, Passenger ship, Ocean liner, RORO ship, Natural ambient noise. Analyze the acoustic recording and determine which category it belongs to.", "gt_text": "Based on the acoustic recording, the sound source is classified as Dredger.", "gt_result": {"class_list": ["Fish boat", "Trawler", "Mussel boat", "Tugboat", "Dredger", "Motorboat", "Pilot boat", "Sailboat", "Passenger ship", "Ocean liner", "RORO ship", "Natural ambient noise"], "gt_class": ["Dredger"]}, "meta_data": {"ori_file_path": "95__A__Draga_3.wav", "seg_index": 2}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00843", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00843.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161022-44_030503_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161022-44_030503_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180921-186_111213_00204", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180921-186_111213_00204.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00219", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00219.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00291", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00291.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170429a-68_102113_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170429a-68_102113_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00255", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00255.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180418-193_004704_00303", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180418-193_004704_00303.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171105a-3_3_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171105a-3_3_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171214a-53_184639_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171214a-53_184639_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111e-9_9_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111e-9_9_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161113a-53_212239_00024", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161113a-53_212239_00024.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00960", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00960.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180411-189_081047_00000", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180411-189_081047_00000.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00534", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00534.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00402", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00402.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00195", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00195.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160507a-4_4_00141", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160507a-4_4_00141.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00159", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00159.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180226-180_033811_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180226-180_033811_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00348", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00348.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160904a-39_035818_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160904a-39_035818_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00783", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00783.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111e-9_9_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111e-9_9_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00369", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00369.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170530a-87_165315_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170530a-87_165315_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00303", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00303.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00948", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00948.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170520a-84_174851_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170520a-84_174851_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160823-31_041452_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160823-31_041452_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161022-44_030503_00186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161022-44_030503_00186.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00195", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00195.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221-61_114923_00213", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221-61_114923_00213.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161009-42_42_00021", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161009-42_42_00021.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00321", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00321.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160521a-10_041411_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160521a-10_041411_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170625-118_180527_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170625-118_180527_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170517a-82_162940_00018", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170517a-82_162940_00018.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00069", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00069.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171214a-101_075400_00255", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171214a-101_075400_00255.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215b-108_114505_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215b-108_114505_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00093", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00093.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00123.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171106-4_4_00075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171106-4_4_00075.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00435", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00435.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170122b-74_050415_00069", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170122b-74_050415_00069.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171213g-100_234857_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171213g-100_234857_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171017-124_095628_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171017-124_095628_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170509-74_174012_00201", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170509-74_174012_00201.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00477", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00477.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180610-215_135143_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180610-215_135143_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00453", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00453.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00489", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00489.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00459", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00459.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170629-120_215813_00294", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170629-120_215813_00294.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00729", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00729.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170510-105_041848_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170510-105_041848_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905b-182_061547_00090", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905b-182_061547_00090.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00426", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00426.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171114c-16_16_00090", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171114c-16_16_00090.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171017-124_095628_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171017-124_095628_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00456", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00456.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905b-182_061547_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905b-182_061547_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00678", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00678.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180219-145_034422_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180219-145_034422_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00489", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00489.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124c-42_134001_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124c-42_134001_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00450", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00450.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408a-66_224944_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408a-66_224944_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210c-90_124043_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210c-90_124043_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126b-51_044753_00219", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126b-51_044753_00219.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171213g-100_234857_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171213g-100_234857_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207b-81_032120_00351", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207b-81_032120_00351.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171210-135_021603_00075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171210-135_021603_00075.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00117.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161002-40_130331_00111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161002-40_130331_00111.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160907-36_094356_00411", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160907-36_094356_00411.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171017-124_095628_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171017-124_095628_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171017-124_095628_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171017-124_095628_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00117.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00282", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00282.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171105a-3_3_00075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171105a-3_3_00075.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160629a-23_23_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160629a-23_23_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00504", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00504.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160907-36_094356_00216", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160907-36_094356_00216.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171114c-16_16_00045", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171114c-16_16_00045.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160629a-23_23_00111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160629a-23_23_00111.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00063.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00486", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00486.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00408", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00408.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170510-75_113409_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170510-75_113409_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00273", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00273.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00651", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00651.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171214a-53_184639_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171214a-53_184639_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00204", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00204.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00135.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170608-113_234244_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170608-113_234244_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171115-9_9_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171115-9_9_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128c-28_190038_00270", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128c-28_190038_00270.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126b-51_044753_00465", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126b-51_044753_00465.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00477", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00477.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00234", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00234.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160521a-10_041411_00234", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160521a-10_041411_00234.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00381", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00381.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00438", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00438.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00630", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00630.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170111-51_193600_00201", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170111-51_193600_00201.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00198", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00198.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00030.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00198", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00198.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171215e-56_170639_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171215e-56_170639_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210c-90_124043_00321", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210c-90_124043_00321.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00240", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00240.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205-69_69_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205-69_69_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00219", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00219.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207b-81_032120_00501", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207b-81_032120_00501.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00162.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171125c-46_134833_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171125c-46_134833_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161019a-46_174803_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161019a-46_174803_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128d-29_193239_00144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128d-29_193239_00144.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00141", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00141.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160603-15_042208_00348", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160603-15_042208_00348.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207-79_010450_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207-79_010450_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180219-145_034422_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180219-145_034422_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00867", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00867.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00441", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00441.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207-79_010450_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207-79_010450_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171210-135_021603_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171210-135_021603_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180921-186_111213_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180921-186_111213_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00273", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00273.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00453", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00453.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00000", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00000.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00324", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00324.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00405", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00405.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00660", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00660.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170517b-83_225337_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170517b-83_225337_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170417-67_025741_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170417-67_025741_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00147", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00147.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170122b-74_050415_00045", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170122b-74_050415_00045.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124d-43_161634_00297", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124d-43_161634_00297.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00939", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00939.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160831-38_034757_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160831-38_034757_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00111", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00111.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171213g-100_234857_00000", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171213g-100_234857_00000.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180914-234_024440_00000", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180914-234_024440_00000.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171203a-40_40_00147", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171203a-40_40_00147.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00216", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00216.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00003", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00003.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171124-22_012051_00612", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171124-22_012051_00612.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160831-38_034757_00198", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160831-38_034757_00198.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160603-15_042208_00231", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160603-15_042208_00231.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161027-46_145435_00225", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161027-46_145435_00225.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170401-64_000636_00009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170401-64_000636_00009.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160927-39_39_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160927-39_39_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170530a-87_165315_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170530a-87_165315_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00606", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00606.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00639", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00639.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170209-80_040238_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170209-80_040238_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00024", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00024.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171129-55_000645_00237", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171129-55_000645_00237.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00267", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00267.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00306", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00306.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00327", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00327.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170510-75_113409_00066", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170510-75_113409_00066.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160904a-39_035818_00018", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160904a-39_035818_00018.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00267", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00267.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170120-70_070941_00147", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170120-70_070941_00147.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00765", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00765.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180503-159_153434_00318", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180503-159_153434_00318.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00588", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00588.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00246", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00246.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171115-9_9_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171115-9_9_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00306", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00306.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00447", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00447.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180417-192_005456_00282", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180417-192_005456_00282.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00543", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00543.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00165.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00108", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00108.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124c-42_134001_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124c-42_134001_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00192", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00192.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124c-42_134001_00363", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124c-42_134001_00363.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160601c-14_14_00009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160601c-14_14_00009.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00345", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00345.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00411", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00411.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180219-145_034422_00063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180219-145_034422_00063.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161019a-46_174803_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161019a-46_174803_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00276", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00276.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00159", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00159.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171111-7_025236_00375", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171111-7_025236_00375.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207b-81_032120_00045", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207b-81_032120_00045.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180125-140_071504_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180125-140_071504_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00201", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00201.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00537", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00537.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171213g-100_234857_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171213g-100_234857_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00423", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00423.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_01023", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_01023.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00294", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00294.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00162.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_01128", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_01128.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00207", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00207.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00123.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00405", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00405.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171215e-56_170639_00063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171215e-56_170639_00063.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171215e-56_170639_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171215e-56_170639_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171124-22_012051_00327", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171124-22_012051_00327.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00444", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00444.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00318", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00318.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170608-113_234244_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170608-113_234244_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00252", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00252.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00183", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00183.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00444", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00444.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180203-174_232020_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180203-174_232020_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171114c-16_16_00237", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171114c-16_16_00237.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00483", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00483.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00216", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00216.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161022-44_030503_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161022-44_030503_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180610-215_135143_00024", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180610-215_135143_00024.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180307-146_075607_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180307-146_075607_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160507a-4_4_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160507a-4_4_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00312", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00312.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00021", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00021.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160831-38_034757_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160831-38_034757_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00255", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00255.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00246", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00246.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00162.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160603-15_042208_00384", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160603-15_042208_00384.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408-65_040520_00141", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408-65_040520_00141.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00645", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00645.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180503-159_153434_00240", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180503-159_153434_00240.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160823-31_041452_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160823-31_041452_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160629a-23_23_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160629a-23_23_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111f-10_10_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111f-10_10_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00141", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00141.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00156", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00156.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205-69_69_00174", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205-69_69_00174.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00534", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00534.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124d-43_161634_00165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124d-43_161634_00165.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171120-131_163321_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171120-131_163321_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180226-180_033811_00063", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180226-180_033811_00063.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00090", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00090.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171106-4_4_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171106-4_4_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00321", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00321.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207b-81_032120_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207b-81_032120_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171214a-101_075400_00066", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171214a-101_075400_00066.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161019a-46_174803_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161019a-46_174803_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161019a-46_174803_00300", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161019a-46_174803_00300.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00246", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00246.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170216-81_035911_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170216-81_035911_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171105a-3_3_00150", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171105a-3_3_00150.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160904a-39_035818_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160904a-39_035818_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171214a-101_075400_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171214a-101_075400_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00573", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00573.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171124-22_012051_00282", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171124-22_012051_00282.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00261", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00261.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00492", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00492.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00303", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00303.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00594", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00594.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171118-27_27_00030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171118-27_27_00030.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205e-71_111659_00036", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205e-71_111659_00036.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00186.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180703-177_221012_00195", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180703-177_221012_00195.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124d-43_161634_00045", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124d-43_161634_00045.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217a-59_050224_00366", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217a-59_050224_00366.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00291", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00291.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408-65_040520_00222", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408-65_040520_00222.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171120a-18_054508_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171120a-18_054508_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170417-67_025741_00237", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170417-67_025741_00237.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00309", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00309.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161002-40_130331_00228", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161002-40_130331_00228.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170209-80_040238_00024", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170209-80_040238_00024.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170509-74_174012_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170509-74_174012_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171117b-25_055206_00309", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171117b-25_055206_00309.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00363", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00363.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00630", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00630.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00267", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00267.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00135.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180307-146_075607_00165", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180307-146_075607_00165.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171206e-78_78_00129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171206e-78_78_00129.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170509-74_174012_00240", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170509-74_174012_00240.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00261", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00261.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00189", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00189.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180418-193_004704_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180418-193_004704_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171214a-53_184639_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171214a-53_184639_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180418-193_004704_00396", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180418-193_004704_00396.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00546", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00546.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126b-51_044753_00054", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126b-51_044753_00054.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00393", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00393.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00435", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00435.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00423", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00423.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00222", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00222.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160603-15_042208_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160603-15_042208_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00156", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00156.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180125-140_071504_00117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180125-140_071504_00117.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00009.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00102", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00102.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00102", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00102.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180417-192_005456_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180417-192_005456_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160904a-39_035818_00255", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160904a-39_035818_00255.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00294", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00294.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180417-192_005456_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180417-192_005456_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00288", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00288.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00459", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00459.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180307-146_075607_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180307-146_075607_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170530a-87_165315_00192", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170530a-87_165315_00192.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171129-55_000645_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171129-55_000645_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616a-95_231343_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616a-95_231343_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180921-186_111213_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180921-186_111213_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00306", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00306.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171207-79_010450_00135", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171207-79_010450_00135.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171204a-43_081356_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171204a-43_081356_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170122b-74_050415_00105", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170122b-74_050415_00105.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00129.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00375", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00375.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171204-66_024305_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171204-66_024305_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205e-71_111659_00240", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205e-71_111659_00240.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128d-29_193239_00183", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128d-29_193239_00183.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180307-146_075607_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180307-146_075607_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161024-45_061514_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161024-45_061514_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20161102-48_124450_00108", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20161102-48_124450_00108.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00417", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00417.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111e-9_9_00276", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111e-9_9_00276.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171118-27_27_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171118-27_27_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00066", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00066.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171127-53_231436_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171127-53_231436_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00336", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00336.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128c-28_190038_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128c-28_190038_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170616-94_174832_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170616-94_174832_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171125c-46_134833_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171125c-46_134833_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111f-10_10_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111f-10_10_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00354", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00354.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00360", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00360.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00357", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00357.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00450", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00450.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170509-74_174012_00090", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170509-74_174012_00090.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00756", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00756.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170331-62_044959_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170331-62_044959_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00204", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00204.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171114c-16_16_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171114c-16_16_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170906-114_104632_00075", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170906-114_104632_00075.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408-65_040520_00180", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408-65_040520_00180.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170510-75_113409_00222", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170510-75_113409_00222.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160907-36_094356_00108", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160907-36_094356_00108.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180905c-183_212331_00084", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180905c-183_212331_00084.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00213", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00213.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00348", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00348.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00774", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00774.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170111-51_193600_00279", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170111-51_193600_00279.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180203-174_232020_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180203-174_232020_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00684", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00684.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171124-22_012051_00543", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171124-22_012051_00543.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180120-139_191134_00051", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180120-139_191134_00051.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00432", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00432.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00069", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00069.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171111e-9_9_00114", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171111e-9_9_00114.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170111-51_193600_00066", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170111-51_193600_00066.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170419-98_021347_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170419-98_021347_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00171", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00171.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170716-103_111744_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170716-103_111744_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170403a-93_102955_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170403a-93_102955_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171115-9_9_00009", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171115-9_9_00009.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170625-118_180527_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170625-118_180527_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180703-177_221012_00183", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180703-177_221012_00183.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00366", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00366.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180326-149_180641_00246", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180326-149_180641_00246.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180206a-176_131726_00087", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180206a-176_131726_00087.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161009-42_42_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161009-42_42_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180515-206_031557_00123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180515-206_031557_00123.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170723a-128_192900_00186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170723a-128_192900_00186.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171118c-15_185844_00042", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171118c-15_185844_00042.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126-49_023314_00012", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126-49_023314_00012.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00663", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00663.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171118-27_27_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171118-27_27_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171120a-18_054508_00261", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171120a-18_054508_00261.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170408-65_040520_00294", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170408-65_040520_00294.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00288", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00288.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171105a-3_3_00039", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171105a-3_3_00039.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00081", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00081.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170512-107_081819_00030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170512-107_081819_00030.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171219a-60_210546_00768", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171219a-60_210546_00768.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00600", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00600.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00300", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00300.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180703-177_221012_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180703-177_221012_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20171210-135_021603_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20171210-135_021603_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171214a-53_184639_00210", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171214a-53_184639_00210.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171205e-71_111659_00213", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171205e-71_111659_00213.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171114-8_072235_00348", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171114-8_072235_00348.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00750", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00750.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171215e-56_170639_00078", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171215e-56_170639_00078.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171111-7_025236_00249", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171111-7_025236_00249.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00408", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00408.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00378", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00378.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160616-15_214515_00324", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160616-15_214515_00324.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00327", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00327.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221a-62_144544_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221a-62_144544_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170417-67_025741_00030", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170417-67_025741_00030.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00048", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00048.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160529-13_083740_00096", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160529-13_083740_00096.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180827-180_182309_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180827-180_182309_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160604-11_171238_00138", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160604-11_171238_00138.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160714a-26_141202_00216", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160714a-26_141202_00216.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171208-84_005033_00219", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171208-84_005033_00219.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180119-167_055815_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180119-167_055815_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160526-6_6_00120", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160526-6_6_00120.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128d-29_193239_00108", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128d-29_193239_00108.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180711-178_094820_00129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180711-178_094820_00129.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180921-186_111213_00303", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180921-186_111213_00303.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171119b-29_151043_00189", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171119b-29_151043_00189.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128d-29_193239_00192", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128d-29_193239_00192.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171121-19_101117_00534", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171121-19_101117_00534.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160617-16_225229_00117", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160617-16_225229_00117.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180722-179_181231_00342", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180722-179_181231_00342.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170511a-76_195844_00225", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170511a-76_195844_00225.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210-88_004442_00069", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210-88_004442_00069.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171126b-51_044753_00456", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171126b-51_044753_00456.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171215c-109_141018_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171215c-109_141018_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171124c-42_134001_00072", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171124c-42_134001_00072.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00153", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00153.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170906-114_104632_00504", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170906-114_104632_00504.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20180630-175_094326_00252", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20180630-175_094326_00252.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180413-191_212814_00006", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180413-191_212814_00006.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160601c-14_14_00033", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160601c-14_14_00033.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171225-64_015934_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171225-64_015934_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171128b-27_120847_00342", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171128b-27_120847_00342.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171221-61_114923_00099", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171221-61_114923_00099.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171204-66_024305_00186", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171204-66_024305_00186.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160529-13_083740_00129", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160529-13_083740_00129.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20160907-36_094356_00237", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20160907-36_094356_00237.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00144", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00144.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161002-40_130331_00162", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161002-40_130331_00162.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170822-133_172736_00123", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170822-133_172736_00123.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160529-13_083740_00015", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160529-13_083740_00015.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171210c-90_124043_00333", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171210c-90_124043_00333.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20170704-122_145807_00261", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20170704-122_145807_00261.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160514-7_113853_00282", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160514-7_113853_00282.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00345", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00345.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170629-99_012112_00057", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170629-99_012112_00057.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171201-32_024235_00264", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171201-32_024235_00264.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171217-58_020446_00132", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171217-58_020446_00132.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171229b-68_200347_00177", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171229b-68_200347_00177.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20161027-46_145435_00168", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20161027-46_145435_00168.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171123-21_035104_00960", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171123-21_035104_00960.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20160521a-10_041411_00327", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20160521a-10_041411_00327.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00060", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00060.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180725-227_013354_00462", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180725-227_013354_00462.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Cargo_20171202c-63_123015_00258", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Cargo_20171202c-63_123015_00258.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Cargo.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Cargo"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171230a-69_163503_00675", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171230a-69_163503_00675.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tanker_20180417-192_005456_00156", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tanker_20180417-192_005456_00156.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tanker.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tanker"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Passengership_20170430-69_011712_00027", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Passengership_20170430-69_011712_00027.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Passengership.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Passengership"]}, "meta_data": {}}
{"task_name": "DeepShip", "dataset_name": "DeepShip", "id": "Tug_20171213a-52_071716_00126", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Underwater_Acoustics-DeepShip-Vessel_Type_Classification/raw_input/Tug_20171213a-52_071716_00126.wav", "length": 96000, "timestamps": [], "fs": 32000}, "input_text": "Given an underwater acoustic recording from the DeepShip dataset, which contains real-world underwater recordings of commercial vessels collected in the Strait of Georgia, classify the vessel into one of the following 4 categories: Cargo, Passengership, Tanker, Tug. Analyze the recording and determine which category it belongs to.", "gt_text": "The recording is classified as Tug.", "gt_result": {"class_list": ["Cargo", "Passengership", "Tanker", "Tug"], "gt_class": ["Tug"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "468144", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Phee_468144.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "291796", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Seep_291796.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "527671", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Infant_cry_527671.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "51999", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_5_Phee_51999.flac", "length": 216000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "292987", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Tsik_292987.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "822093", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Phee_822093.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "495470", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Phee_495470.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "530733", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_0_Phee_530733.flac", "length": 148801, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "333626", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Infant_cry_333626.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "405547", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_3_Twitter_405547.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "490682", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_0_Trill_490682.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "477589", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Phee_477589.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "694179", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_2_0_Trill_694179.flac", "length": 115199, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "778022", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_6_Infant_cry_778022.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "440340", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_4_Infant_cry_440340.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "480180", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Tsik_480180.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "859866", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Phee_859866.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "548311", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_7_0_Phee_548311.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "200536", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_0_Trill_200536.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "637260", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Infant_cry_637260.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "648203", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Trill_648203.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "537062", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Trill_537062.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "465730", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Trill_465730.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "602236", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_1_Tsik_602236.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "81258", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_2_Seep_81258.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "65428", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_0_Tsik_65428.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "792306", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_7_Phee_792306.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "842177", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_8_Phee_842177.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "684457", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Phee_684457.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "244290", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Trill_244290.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "315587", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Trill_315587.flac", "length": 105600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "622493", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Tsik_622493.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "824367", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_7_Tsik_824367.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "236301", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Twitter_236301.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "340467", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Phee_340467.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "713214", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_2_Twitter_713214.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "214245", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Seep_214245.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "563222", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Seep_563222.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "307253", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_8_Infant_cry_307253.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "241934", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Tsik_241934.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "845797", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_9_Twitter_845797.flac", "length": 196800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "494941", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_2_Trill_494941.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "324416", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_324416.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "161384", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_1_Phee_161384.flac", "length": 206400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "296420", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Infant_cry_296420.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "607130", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_2_0_Twitter_607130.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "648280", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Phee_648280.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "677488", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_3_Phee_677488.flac", "length": 216000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "89947", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_2_Seep_89947.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "344098", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Twitter_344098.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "257025", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_1_Trill_257025.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "307748", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_8_Twitter_307748.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "213411", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2019_12_0_Seep_213411.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "520676", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Phee_520676.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "775329", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_6_Twitter_775329.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "537095", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Phee_537095.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "568419", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_9_0_Tsik_568419.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "495419", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Seep_495419.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "599516", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_0_Trill_599516.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "627130", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_1_Phee_627130.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "198878", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Infant_cry_198878.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "67886", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_5_Phee_67886.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "317565", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_317565.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "463312", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Trill_463312.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "560241", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Tsik_560241.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "226242", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Twitter_226242.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "32507", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Seep_32507.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "142549", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_8_Seep_142549.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "49473", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_4_Tsik_49473.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "558544", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Infant_cry_558544.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "780636", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_6_Trill_780636.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "572806", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_1_Twitter_572806.flac", "length": 240000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "246528", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Seep_246528.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "513669", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Tsik_513669.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "202898", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Twitter_202898.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "561744", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Tsik_561744.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "517463", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Seep_517463.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "718737", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Tsik_718737.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "553385", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Trill_553385.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "529002", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Seep_529002.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "311566", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_8_Trill_311566.flac", "length": 134400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "315445", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Seep_315445.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "421881", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_2_Tsik_421881.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "644865", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Twitter_644865.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "595029", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Phee_595029.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "556764", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Seep_556764.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "30062", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Trill_30062.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "238036", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Twitter_238036.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "27721", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_2_Phee_27721.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "679249", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_2_Twitter_679249.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "236768", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Phee_236768.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "88450", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_2_Twitter_88450.flac", "length": 119999, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "670886", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_2_Trill_670886.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "497761", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_2_Trill_497761.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "127394", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_6_Trill_127394.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "245126", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Tsik_245126.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "683306", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_3_Infant_cry_683306.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "527901", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Seep_527901.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "845235", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_9_Tsik_845235.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "136336", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Infant_cry_136336.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "508718", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Tsik_508718.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "624021", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Trill_624021.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "558376", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Tsik_558376.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "734754", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_4_Twitter_734754.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "369493", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_13_Tsik_369493.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "867739", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Twitter_867739.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "345384", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Infant_cry_345384.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "549757", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Phee_549757.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "671625", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_2_Trill_671625.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "504829", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_3_Infant_cry_504829.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "401075", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_2_Seep_401075.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "583598", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_1_Phee_583598.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "31100", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Tsik_31100.flac", "length": 177599, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "642729", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Phee_642729.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "157428", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_0_Seep_157428.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "383567", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_9_0_Twitter_383567.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "861427", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Infant_cry_861427.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "505477", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_3_Seep_505477.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "553755", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Seep_553755.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "537527", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Trill_537527.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "17610", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_1_Phee_17610.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "318650", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_318650.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "123168", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_6_Twitter_123168.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "69736", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_1_Tsik_69736.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "655516", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_1_Phee_655516.flac", "length": 148799, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "839626", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_8_Seep_839626.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "5268", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_0_Twitter_5268.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "502249", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Twitter_502249.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "429151", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_3_Phee_429151.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "346323", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Seep_346323.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "60235", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_0_Tsik_60235.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "416347", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_2_Trill_416347.flac", "length": 100801, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "591105", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_7_0_Seep_591105.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "266018", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Infant_cry_266018.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "665412", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Infant_cry_665412.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "819374", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Phee_819374.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "141114", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Twitter_141114.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "818684", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Tsik_818684.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "320493", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_320493.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "568941", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Infant_cry_568941.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "279620", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Tsik_279620.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "484529", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_7_Seep_484529.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "7747", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_0_Phee_7747.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "597641", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Trill_597641.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "287515", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Infant_cry_287515.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "667425", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Trill_667425.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "529199", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Tsik_529199.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "153342", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_0_Trill_153342.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "589601", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_7_0_Seep_589601.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "812228", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_5_Seep_812228.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "461401", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Infant_cry_461401.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "720228", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_9_0_Tsik_720228.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "153077", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_8_Seep_153077.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "796030", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_4_Tsik_796030.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "863950", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_2_Tsik_863950.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "760154", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_4_Twitter_760154.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "50439", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_5_Phee_50439.flac", "length": 177599, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "30684", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Twitter_30684.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "606103", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_1_Tsik_606103.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "298516", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Infant_cry_298516.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "569210", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Seep_569210.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "817251", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Tsik_817251.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "870190", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Trill_870190.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "353285", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_12_Tsik_353285.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "484798", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_7_Infant_cry_484798.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "448140", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Seep_448140.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "322838", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Infant_cry_322838.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "176152", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_2_Seep_176152.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "614539", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_1_Infant_cry_614539.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "247127", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_0_Tsik_247127.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "599694", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Infant_cry_599694.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "625997", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Infant_cry_625997.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "833741", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_8_Twitter_833741.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "809147", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_5_Twitter_809147.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "227276", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Phee_227276.flac", "length": 167999, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "366636", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_13_Trill_366636.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "553634", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Tsik_553634.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "602999", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_1_Phee_602999.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "153060", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_8_Seep_153060.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "678999", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_2_Twitter_678999.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "94205", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_3_Trill_94205.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "74516", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_1_Twitter_74516.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "321412", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_9_Tsik_321412.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "822756", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_7_Twitter_822756.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "253953", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_1_Twitter_253953.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "219637", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_0_Seep_219637.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "804950", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_5_Phee_804950.flac", "length": 148801, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "211621", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_0_Phee_211621.flac", "length": 120001, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "753310", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_4_Seep_753310.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "799623", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_7_Seep_799623.flac", "length": 201600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "539420", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_3_0_Phee_539420.flac", "length": 240000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "759941", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_4_Tsik_759941.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "118185", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_5_Trill_118185.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "14743", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_1_Seep_14743.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "548949", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_7_0_Tsik_548949.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "637217", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Trill_637217.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "799534", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_7_Infant_cry_799534.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "834507", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_8_Twitter_834507.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "854779", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_10_Twitter_854779.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "558322", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Infant_cry_558322.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "638770", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_1_Phee_638770.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "608229", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Seep_608229.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "86656", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_2_Twitter_86656.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "731523", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_2_1_Twitter_731523.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "290146", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_0_Infant_cry_290146.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "31619", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Phee_31619.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "687025", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_2_Trill_687025.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "215932", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Seep_215932.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "206863", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_0_Tsik_206863.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "425592", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_9_0_Tsik_425592.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "564773", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_0_Infant_cry_564773.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "766331", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_5_Infant_cry_766331.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "786639", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_2_Tsik_786639.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "312988", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_8_Infant_cry_312988.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "621108", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_2_Tsik_621108.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "868322", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Seep_868322.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "137048", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Infant_cry_137048.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "332721", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Twitter_332721.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "683581", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_3_Seep_683581.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "505351", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_3_Tsik_505351.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "411681", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_3_Twitter_411681.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "476207", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Trill_476207.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "183832", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_3_Trill_183832.flac", "length": 201600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "587418", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Trill_587418.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "366071", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_13_Seep_366071.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "277794", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_0_Twitter_277794.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "279461", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Tsik_279461.flac", "length": 144000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "335621", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_1_Infant_cry_335621.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "282348", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Phee_282348.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "237777", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Seep_237777.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "551789", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Tsik_551789.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "817888", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_6_Twitter_817888.flac", "length": 187200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "716276", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Trill_716276.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "584058", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_1_Tsik_584058.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "509317", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_3_Trill_509317.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "128751", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_6_Tsik_128751.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "287525", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Infant_cry_287525.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "552157", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_0_Tsik_552157.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "136453", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Phee_136453.flac", "length": 196800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "195628", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_0_Seep_195628.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "795548", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_7_Phee_795548.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "15028", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_1_Twitter_15028.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "790812", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Infant_cry_790812.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "201787", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Infant_cry_201787.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "369017", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_13_Infant_cry_369017.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "650896", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_1_Seep_650896.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "32801", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Twitter_32801.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "243265", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Seep_243265.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "170769", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_1_Infant_cry_170769.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "296993", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Seep_296993.flac", "length": 67200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "761764", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_2_2_Twitter_761764.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "858188", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_10_Trill_858188.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "68450", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_0_Phee_68450.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "689436", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_3_Infant_cry_689436.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "487399", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_7_Phee_487399.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "668696", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Trill_668696.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "140935", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Infant_cry_140935.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "233014", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_0_Trill_233014.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "330142", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Twitter_330142.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "340127", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_11_Twitter_340127.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "581220", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_0_Trill_581220.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "300282", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Seep_300282.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "624123", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Tsik_624123.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "576659", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_12_0_Infant_cry_576659.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "329730", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Seep_329730.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "419822", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_10_2_Phee_419822.flac", "length": 139200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "31590", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_1_3_Phee_31590.flac", "length": 177600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "717590", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_3_Trill_717590.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "167990", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_1_Phee_167990.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "739708", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_3_Phee_739708.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "660121", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Tsik_660121.flac", "length": 81600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "251135", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_1_Trill_251135.flac", "length": 96000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "868149", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Seep_868149.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "806704", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_5_Trill_806704.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "475392", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Twitter_475392.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "465712", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_5_Phee_465712.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "594447", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_1_Infant_cry_594447.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "355954", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_1_1_Seep_355954.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "524184", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_0_Phee_524184.flac", "length": 158400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "437308", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_9_1_Twitter_437308.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "332615", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_10_Twitter_332615.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "628314", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2021_1_1_Infant_cry_628314.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "383286", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_1_Twitter_383286.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "299965", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_7_Trill_299965.flac", "length": 148800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "531940", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_1_Trill_531940.flac", "length": 100800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "473834", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_6_Infant_cry_473834.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "409628", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_4_3_Trill_409628.flac", "length": 129600, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "623889", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_10_2_Trill_623889.flac", "length": 91200, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "869766", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_3_Seep_869766.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "844416", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_4_9_Twitter_844416.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "278688", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_6_Trill_278688.flac", "length": 144000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "770396", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2023_3_5_Tsik_770396.flac", "length": 72000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "643271", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_2_1_Twitter_643271.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "443323", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_9_1_Trill_443323.flac", "length": 110400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Trill.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Trill"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "137811", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_2_7_Twitter_137811.flac", "length": 168000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Twitter.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Twitter"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "267073", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Phee_267073.flac", "length": 120000, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Phee.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Phee"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "279463", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_12_1_Tsik_279463.flac", "length": 76800, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Tsik.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Tsik"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "220995", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_5_Infant_cry_220995.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "212233", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2022_3_4_Infant_cry_212233.flac", "length": 86400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Infant_cry.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Infant_cry"]}, "meta_data": {}}
{"task_name": "MarmAudio", "dataset_name": "MarmAudio", "id": "665138", "data_type": "flac", "input_ts": {"num_channel": 1, "channel_detail": [], "path": "Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification/raw_input/2020_11_1_Seep_665138.flac", "length": 62400, "timestamps": [], "fs": 96000}, "input_text": "Now, I need your assistance to complete a marmoset classification task. The provided audio is a recording of a marmoset call. It is known that the provided data corresponds to one of the following categories: Twitter, Tsik, Phee, Trill, Infant_cry or Seep. Please determine which of the following call types it belongs to.", "gt_text": "This audio is classified as Seep.", "gt_result": {"class_list": ["Twitter", "Tsik", "Phee", "Trill", "Infant_cry", "Seep"], "gt_class": ["Seep"]}, "meta_data": {}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_800~1500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_800~1500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_800~1500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000_60_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000_60_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000_60_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_40_2_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_40_2_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_40_2_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_40_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_40_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_40_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000~2000_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000~2000_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000~2000_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1797_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1797_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1797_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1797_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1797_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1797_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_60_2_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_60_2_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_60_2_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1800_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1800_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1800_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2500_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2500_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2500_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_40_2_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_40_2_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_40_2_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_1_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_1_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_1_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_60_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_60_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_60_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1797_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1797_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1797_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_800~1500_20_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_800~1500_20_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_800~1500_20_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_800~1500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_800~1500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_800~1500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500~2500_60_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500~2500_60_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500~2500_60_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500~2500_40_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500~2500_40_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500~2500_40_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_60_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_60_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_60_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_20_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_20_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_20_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_40_2_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_40_2_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_40_2_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_1_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_1_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_1_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_800-1500_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_800-1500_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_800-1500_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1797_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1797_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1797_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2500_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2500_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2500_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000_20_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000_20_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000_20_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_20_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_20_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_20_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_800~1500_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_800~1500_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_800~1500_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1797_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1797_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1797_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_3000_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_3000_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_3000_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_20_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_20_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_20_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_0_1_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_0_1_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_0_1_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000~2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000~2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000~2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_20_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_20_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_20_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_1_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_1_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_1_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_20_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_20_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_20_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_60_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_60_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_60_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1797_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1797_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1797_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2000_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2000_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2000_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1797_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1797_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1797_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2000_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2000_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2000_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1797_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1797_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1797_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_40_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_40_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_40_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2500_20_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2500_20_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2500_20_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_20_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_20_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_20_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_20_2_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_20_2_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_20_2_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_40_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_40_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_40_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_40_1_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_40_1_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_40_1_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_20_1_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_20_1_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_20_1_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_20_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_20_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_20_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_0_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_0_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_0_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_60_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_60_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_60_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1800_60_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1800_60_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1800_60_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1797_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1797_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1797_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_60_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_60_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_60_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_60_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_60_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_60_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2500_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2500_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2500_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1797_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1797_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1797_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_60_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_60_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_60_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_60_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_60_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_60_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_20_3_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_20_3_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_20_3_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_40_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_40_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_40_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_20_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_20_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_20_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_60_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_60_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_60_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500~2500_20_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500~2500_20_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500~2500_20_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_0_1_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_0_1_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_0_1_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_20_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_20_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_20_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2500_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2500_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2500_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000_20_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000_20_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000_20_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_60_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_60_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_60_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_60_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_60_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_60_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_40_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_40_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_40_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_0_2_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_0_2_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_0_2_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_3000_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_3000_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_3000_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2000_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2000_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2000_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_20_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_20_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_20_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_20_2_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_20_2_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_20_2_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_60_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_60_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_60_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_40_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_40_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_40_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1797_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1797_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1797_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_0_2_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_0_2_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_0_2_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1800_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1800_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1800_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_20_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_20_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_20_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_60_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_60_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_60_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1800_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1800_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1800_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_40_2_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_40_2_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_40_2_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2000_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2000_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2000_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_3000_20_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_3000_20_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_3000_20_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_40_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_40_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_40_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2500_60_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2500_60_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2500_60_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_60_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_60_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_60_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_20_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_20_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_20_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_20_1_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_20_1_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_20_1_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2500_20_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2500_20_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2500_20_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1800_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1800_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1800_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1800_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1800_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1800_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2000_20_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2000_20_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2000_20_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_40_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_40_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_40_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_40_2_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_40_2_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_40_2_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_20_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_20_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_20_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_60_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_60_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_60_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_60_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_60_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_60_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_3000_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_3000_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_3000_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_0_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_0_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_0_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_40_1_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_40_1_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_40_1_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_60_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_60_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_60_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_20_1_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_20_1_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_20_1_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_800~1500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_800~1500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_800~1500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_20_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_20_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_20_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_40_2_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_40_2_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_40_2_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500~2500_0_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500~2500_0_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500~2500_0_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_800-1500_60_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_800-1500_60_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_800-1500_60_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_40_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_40_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_40_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2500_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2500_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2500_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_40_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_40_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_40_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_20_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_20_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_20_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_60_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_60_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_60_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1797_0_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1797_0_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1797_0_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_40_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_40_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_40_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2000_20_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2000_20_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2000_20_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2500_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2500_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2500_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_40_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_40_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_40_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000~2000_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000~2000_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000~2000_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_60_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_60_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_60_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_60_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_60_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_60_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_60_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_60_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_60_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2500_40_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2500_40_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2500_40_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_60_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_60_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_60_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1797_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1797_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1797_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1800_0_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1800_0_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1800_0_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2000_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2000_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2000_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500_20_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500_20_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500_20_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2500_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2500_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2500_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_20_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_20_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_20_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_3000_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_3000_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_3000_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_20_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_20_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_20_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_60_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_60_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_60_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_40_2_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_40_2_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_40_2_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_60_2_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_60_2_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_60_2_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_60_2_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_60_2_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_60_2_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_40_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_40_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_40_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_40_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_40_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_40_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000~2000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000~2000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000~2000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_2000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_2000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_2000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_800~1500_40_2_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_800~1500_40_2_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_800~1500_40_2_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_60_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_60_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_60_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1000~2000_20_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1000~2000_20_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1000~2000_20_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_60_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_60_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_60_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_40_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_40_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_40_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_800~1500_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_800~1500_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_800~1500_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_800~1500_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_800~1500_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_800~1500_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1500_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1500_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1500_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_60_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_60_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_60_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_0_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_0_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_0_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_800~1500_40_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_800~1500_40_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_800~1500_40_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_800~1500_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_800~1500_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_800~1500_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000_40_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000_40_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000_40_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1800_0_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1800_0_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1800_0_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_0_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_0_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_0_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_3000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_3000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_3000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_60_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_60_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_60_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_3000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_3000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_3000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1800_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1800_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1800_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_60_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_60_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_60_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_20_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_20_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_20_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_0_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_0_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_0_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_0_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_0_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_0_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_0_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_0_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_0_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2500_40_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2500_40_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2500_40_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_60_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_60_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_60_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1800_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1800_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1800_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_40_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_40_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_40_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_40_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_40_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_40_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_20_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_20_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_20_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_3000_20_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_3000_20_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_3000_20_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_2000_40_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_2000_40_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_2000_40_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1000~2000_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1000~2000_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1000~2000_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_20_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_20_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_20_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_2000_60_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_2000_60_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_2000_60_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_20_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_20_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_20_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500~2500_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500~2500_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500~2500_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_20_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_20_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_20_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_40_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_40_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_40_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_40_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_40_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_40_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1500~2500_40_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1500~2500_40_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1500~2500_40_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_3000_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_3000_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_3000_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_0_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_0_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_0_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1000-2000_0_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1000-2000_0_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1000-2000_0_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_60_2_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_60_2_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_60_2_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1800_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1800_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1800_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_20_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_20_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_20_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000~2000_0_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000~2000_0_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000~2000_0_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1797_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1797_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1797_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1797_0_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1797_0_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1797_0_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_3000_20_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_3000_20_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_3000_20_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1797_0_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1797_0_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1797_0_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_20_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_20_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_20_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2500_0_channel1_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2500_0_channel1_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2500_0_channel1_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2500_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2500_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2500_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1800_40_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1800_40_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1800_40_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_40_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_40_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_40_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1797_0_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1797_0_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1797_0_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000~2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000~2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000~2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel3_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel3_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel3_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_1500-2500_0_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_1500-2500_0_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_1500-2500_0_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1500~2500_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1500~2500_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1500~2500_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_0_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_0_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_0_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_40_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_40_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_40_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_800~1500_0_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_800~1500_0_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_800~1500_0_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_40_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_40_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_40_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2500_60_channel5_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2500_60_channel5_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2500_60_channel5_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1000_20_channel1_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1000_20_channel1_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1000_20_channel1_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2500_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2500_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2500_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_2500_60_channel4_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_2500_60_channel4_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_2500_60_channel4_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_1000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_1000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_1000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_800~1500_40_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_800~1500_40_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_800~1500_40_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000~2000_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000~2000_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000~2000_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_20_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_20_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_20_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500_0_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500_0_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500_0_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_3000_0_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_3000_0_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_3000_0_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_20_channel1_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_20_channel1_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_20_channel1_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2000_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2000_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2000_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_2500_60_channel5_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_2500_60_channel5_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_2500_60_channel5_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.6_OF0.6_2000_20_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.6_OF0.6_2000_20_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.6"]}, "meta_data": {"original_filename": "OF0.6_OF0.6_2000_20_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1500_20_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1500_20_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1500_20_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_1500~2500_0_channel2_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_1500~2500_0_channel2_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_1500~2500_0_channel2_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_0_1_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_0_1_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_0_1_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.6_IF0.6_3000_60_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.6_IF0.6_3000_60_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.6"]}, "meta_data": {"original_filename": "IF0.6_IF0.6_3000_60_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1800_20_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1800_20_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1800_20_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_2000_20_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_2000_20_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_2000_20_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000_60_channel2_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000_60_channel2_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000_60_channel2_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000~2000_20_2_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000~2000_20_2_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000~2000_20_2_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.4_RF0.4_1500~2500_0_channel2_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.4_RF0.4_1500~2500_0_channel2_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.4"]}, "meta_data": {"original_filename": "RF0.4_RF0.4_1500~2500_0_channel2_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_40_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_40_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_40_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_800~1500_60_channel2_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_800~1500_60_channel2_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_800~1500_60_channel2_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_2000_0_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_2000_0_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_2000_0_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2500_40_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2500_40_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2500_40_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1800_40_channel6_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1800_40_channel6_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1800_40_channel6_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_2000_60_channel6_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_2000_60_channel6_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_2000_60_channel6_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.4_OF0.4_1500~2500_20_channel4_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.4_OF0.4_1500~2500_20_channel4_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.4"]}, "meta_data": {"original_filename": "OF0.4_OF0.4_1500~2500_20_channel4_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_0_2_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_0_2_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_0_2_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1500~2500_20_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1500~2500_20_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1500~2500_20_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_1000~2000_20_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_1000~2000_20_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_1000~2000_20_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1000_60_channel3_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1000_60_channel3_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1000_60_channel3_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1800_40_channel6_ch0_0", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1800_40_channel6_ch0_0.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1800_40_channel6_ch0_0.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.2_RF0.2_1800_40_channel3_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.2_RF0.2_1800_40_channel3_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.2"]}, "meta_data": {"original_filename": "RF0.2_RF0.2_1800_40_channel3_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.2_IF0.2_1000_60_channel5_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.2_IF0.2_1000_60_channel5_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.2"]}, "meta_data": {"original_filename": "IF0.2_IF0.2_1000_60_channel5_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1800_40_channel6_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1800_40_channel6_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1800_40_channel6_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "NC_NC_1500~2500_60_2_channel5_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/NC_NC_1500~2500_60_2_channel5_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: NC", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["NC"]}, "meta_data": {"original_filename": "NC_NC_1500~2500_60_2_channel5_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_3000_40_channel4_ch0_2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_3000_40_channel4_ch0_2.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_3000_40_channel4_ch0_2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "IF0.4_IF0.4_800~1500_60_channel1_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/IF0.4_IF0.4_800~1500_60_channel1_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IF0.4", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["IF0.4"]}, "meta_data": {"original_filename": "IF0.4_IF0.4_800~1500_60_channel1_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "RF0.6_RF0.6_1000_40_channel3_ch0_3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/RF0.6_RF0.6_1000_40_channel3_ch0_3.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: RF0.6", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["RF0.6"]}, "meta_data": {"original_filename": "RF0.6_RF0.6_1000_40_channel3_ch0_3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "SDUST_bear", "dataset_name": "SDUST", "id": "OF0.2_OF0.2_2000_60_channel4_ch0_1", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis/raw_input/OF0.2_OF0.2_2000_60_channel4_ch0_1.wav", "length": 256000, "timestamps": [], "fs": 25600}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task. It is known that the provided vibration signal sample corresponds to one of the following categories: NC, IF0.2, IF0.4, IF0.6, OF0.2, OF0.4, OF0.6, RF0.2, RF0.4, RF0.6. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OF0.2", "gt_result": {"class_list": ["NC", "IF0.2", "IF0.4", "IF0.6", "OF0.2", "OF0.4", "OF0.6", "RF0.2", "RF0.4", "RF0.6"], "gt_class": ["OF0.2"]}, "meta_data": {"original_filename": "OF0.2_OF0.2_2000_60_channel4_ch0_1.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_9_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_12_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M01_F10_KB24_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M01_F10_KB24_2_channel3.wav", "length": 256087, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M01_F10_KB24_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_4_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_13_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N09_M07_F10_K003_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N09_M07_F10_K003_19_channel2.wav", "length": 256867, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N09_M07_F10_K003_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_18_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_15_channel2.wav", "length": 256093, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M01_F10_K002_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M01_F10_K002_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M01_F10_K002_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_4_channel3.wav", "length": 256079, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_5_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_8_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N09_M07_F10_K004_9_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N09_M07_F10_K004_9_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N09_M07_F10_K004_9_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_5_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_16_channel2.wav", "length": 256090, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_3_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_10_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F10_KI18_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F10_KI18_13_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F10_KI18_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F10_K001_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F10_K001_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F10_K001_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_4_channel3.wav", "length": 256388, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_15_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_6_channel2.wav", "length": 256593, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_16_channel2.wav", "length": 256176, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M07_F10_K004_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M07_F10_K004_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M07_F10_K004_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N15_M01_F10_K005_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N15_M01_F10_K005_14_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N15_M01_F10_K005_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N09_M07_F10_KA22_9_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N09_M07_F10_KA22_9_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N09_M07_F10_KA22_9_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N09_M07_F10_KI21_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N09_M07_F10_KI21_19_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N09_M07_F10_KI21_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_4_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N09_M07_F10_KI08_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N09_M07_F10_KI08_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N09_M07_F10_KI08_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_5_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_13_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M07_F10_K004_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M07_F10_K004_19_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M07_F10_K004_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_16_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M01_F10_K002_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M01_F10_K002_18_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M01_F10_K002_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_4_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_1_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_16_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_19_channel2.wav", "length": 256951, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_7_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N09_M07_F10_KI08_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N09_M07_F10_KI08_7_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N09_M07_F10_KI08_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_13_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_19_channel3.wav", "length": 256081, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_3_channel2.wav", "length": 256085, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_8_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N09_M07_F10_KI01_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N09_M07_F10_KI01_6_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N09_M07_F10_KI01_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_18_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_20_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N09_M07_F10_KB23_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N09_M07_F10_KB23_6_channel2.wav", "length": 256075, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N09_M07_F10_KB23_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_19_channel3.wav", "length": 261419, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_2_channel2.wav", "length": 256088, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_4_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_15_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_12_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F10_K001_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F10_K001_1_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F10_K001_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_3_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M07_F10_K004_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M07_F10_K004_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M07_F10_K004_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_11_channel2.wav", "length": 256018, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_7_channel3.wav", "length": 256125, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_1_channel2.wav", "length": 257207, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M01_F10_KB24_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M01_F10_KB24_14_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M01_F10_KB24_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_7_channel2.wav", "length": 256094, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_3_channel2.wav", "length": 256005, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_17_channel2.wav", "length": 256104, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_17_channel2.wav", "length": 256068, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI03_N15_M01_F10_KI03_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI03_N15_M01_F10_KI03_15_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI03_N15_M01_F10_KI03_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_7_channel3.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_11_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N09_M07_F10_K004_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N09_M07_F10_K004_16_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N09_M07_F10_K004_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M01_F10_KB24_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M01_F10_KB24_19_channel2.wav", "length": 257691, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M01_F10_KB24_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_3_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_8_channel2.wav", "length": 255998, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_2_channel3.wav", "length": 260765, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_11_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_11_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_11_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_8_channel3.wav", "length": 256085, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F10_K001_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F10_K001_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F10_K001_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_16_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI03_N15_M01_F10_KI03_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI03_N15_M01_F10_KI03_8_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI03_N15_M01_F10_KI03_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M01_F10_K002_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M01_F10_K002_5_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M01_F10_K002_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_17_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_10_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F10_KI18_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F10_KI18_19_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F10_KI18_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_3_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA30_N15_M07_F04_KA30_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA30_N15_M07_F04_KA30_10_channel2.wav", "length": 256070, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA30_N15_M07_F04_KA30_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_17_channel2.wav", "length": 256005, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N09_M07_F10_K003_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N09_M07_F10_K003_20_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N09_M07_F10_K003_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_6_channel2.wav", "length": 256293, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F10_KB23_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F10_KB23_6_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F10_KB23_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_13_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_8_channel3.wav", "length": 280916, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N09_M07_F10_KB23_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N09_M07_F10_KB23_2_channel3.wav", "length": 256086, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N09_M07_F10_KB23_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_18_channel2.wav", "length": 256107, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M01_F10_KA05_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M01_F10_KA05_12_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M01_F10_KA05_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_12_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_7_channel3.wav", "length": 256089, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N09_M07_F10_KA22_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N09_M07_F10_KA22_6_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N09_M07_F10_KA22_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_5_channel2.wav", "length": 261433, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_4_channel2.wav", "length": 256095, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI03_N15_M01_F10_KI03_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI03_N15_M01_F10_KI03_7_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI03_N15_M01_F10_KI03_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_14_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N09_M07_F10_KB23_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N09_M07_F10_KB23_4_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N09_M07_F10_KB23_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_8_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M01_F10_KA09_11_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M01_F10_KA09_11_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M01_F10_KA09_11_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F10_KA16_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F10_KA16_2_channel3.wav", "length": 256091, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F10_KA16_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N09_M07_F10_KA05_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N09_M07_F10_KA05_10_channel2.wav", "length": 256086, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N09_M07_F10_KA05_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_5_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_5_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_5_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_3_channel2.wav", "length": 256093, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_1_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M01_F10_KA05_5_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M01_F10_KA05_5_channel3.wav", "length": 256085, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M01_F10_KA05_5_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_20_channel3.wav", "length": 256088, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N09_M07_F10_KA22_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N09_M07_F10_KA22_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N09_M07_F10_KA22_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_20_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_15_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_11_channel2.wav", "length": 249940, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_2_channel3.wav", "length": 256078, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_6_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_2_channel2.wav", "length": 256082, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M01_F10_KA16_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M01_F10_KA16_18_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M01_F10_KA16_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_19_channel3.wav", "length": 256106, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_6_channel3.wav", "length": 256724, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_7_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N15_M07_F10_KI16_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N15_M07_F10_KI16_9_channel2.wav", "length": 274134, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N15_M07_F10_KI16_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M01_F10_KA05_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M01_F10_KA05_16_channel2.wav", "length": 256075, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M01_F10_KA05_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_15_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M07_F10_KA01_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M07_F10_KA01_20_channel3.wav", "length": 256113, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M07_F10_KA01_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_9_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N09_M07_F10_KI01_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N09_M07_F10_KI01_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N09_M07_F10_KI01_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N15_M01_F10_K005_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N15_M01_F10_K005_5_channel2.wav", "length": 261042, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N15_M01_F10_K005_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N15_M07_F04_KI08_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N15_M07_F04_KI08_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N15_M07_F04_KI08_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_3_channel2.wav", "length": 256135, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_2_channel3.wav", "length": 256080, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N15_M07_F04_KI08_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N15_M07_F04_KI08_18_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N15_M07_F04_KI08_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_13_channel2.wav", "length": 255997, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_7_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI03_N15_M01_F10_KI03_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI03_N15_M01_F10_KI03_6_channel3.wav", "length": 256091, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI03_N15_M01_F10_KI03_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_3_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M01_F10_KA09_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M01_F10_KA09_17_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M01_F10_KA09_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA30_N15_M07_F04_KA30_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA30_N15_M07_F04_KA30_2_channel3.wav", "length": 256085, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA30_N15_M07_F04_KA30_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_16_channel2.wav", "length": 262735, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_4_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_16_channel3.wav", "length": 256087, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_6_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_16_channel3.wav", "length": 259404, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_14_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_6_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_4_channel3.wav", "length": 256080, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N15_M01_F10_K005_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N15_M01_F10_K005_20_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N15_M01_F10_K005_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_9_channel2.wav", "length": 256078, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N15_M07_F04_KI01_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N15_M07_F04_KI01_9_channel2.wav", "length": 256246, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N15_M07_F04_KI01_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_12_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_16_channel3.wav", "length": 256673, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_4_channel2.wav", "length": 277358, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_19_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_2_channel2.wav", "length": 256089, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_14_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N15_M07_F10_KI16_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N15_M07_F10_KI16_3_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N15_M07_F10_KI16_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M01_F10_K002_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M01_F10_K002_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M01_F10_K002_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M01_F10_K004_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M01_F10_K004_20_channel2.wav", "length": 257678, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M01_F10_K004_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_20_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_14_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_6_channel3.wav", "length": 256575, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_17_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_17_channel3.wav", "length": 256089, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_17_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N09_M07_F10_KA01_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N09_M07_F10_KA01_16_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N09_M07_F10_KA01_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_19_channel2.wav", "length": 256006, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_16_channel3.wav", "length": 250604, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_1_channel2.wav", "length": 258669, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F10_KA16_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F10_KA16_17_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F10_KA16_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_10_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_7_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_1_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F10_K001_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F10_K001_5_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F10_K001_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F04_KA16_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F04_KA16_13_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F04_KA16_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M07_F10_KA01_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M07_F10_KA01_1_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M07_F10_KA01_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M01_F10_K004_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M01_F10_K004_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M01_F10_K004_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N09_M07_F10_K003_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N09_M07_F10_K003_8_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N09_M07_F10_K003_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N15_M07_F04_KI01_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N15_M07_F04_KI01_6_channel2.wav", "length": 256099, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N15_M07_F04_KI01_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_10_channel2.wav", "length": 256083, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_13_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N09_M07_F10_KB23_9_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N09_M07_F10_KB23_9_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N09_M07_F10_KB23_9_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_5_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_1_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F10_KA16_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F10_KA16_13_channel2.wav", "length": 256089, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F10_KA16_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_2_channel3.wav", "length": 260927, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M07_F04_KA22_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M07_F04_KA22_8_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M07_F04_KA22_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_4_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M01_F10_KB24_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M01_F10_KB24_11_channel2.wav", "length": 256184, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M01_F10_KB24_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_16_channel3.wav", "length": 256093, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N09_M07_F10_KA05_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N09_M07_F10_KA05_2_channel3.wav", "length": 256088, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N09_M07_F10_KA05_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F04_KB24_16_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F04_KB24_16_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F04_KB24_16_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_15_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_6_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_18_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_15_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_15_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_15_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_18_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_8_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N09_M07_F10_KI01_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N09_M07_F10_KI01_15_channel3.wav", "length": 256103, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N09_M07_F10_KI01_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M01_F10_K004_11_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M01_F10_K004_11_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M01_F10_K004_11_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA16_N15_M07_F10_KA16_17_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA16_N15_M07_F10_KA16_17_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA16_N15_M07_F10_KA16_17_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M07_F04_KA22_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M07_F04_KA22_14_channel2.wav", "length": 255999, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M07_F04_KA22_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M07_F10_KA15_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M07_F10_KA15_19_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M07_F10_KA15_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_17_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_17_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_17_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_10_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M07_F10_KA08_7_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M07_F10_KA08_7_channel3.wav", "length": 256094, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M07_F10_KA08_7_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_13_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_19_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_11_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_11_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_11_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_7_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_7_channel2.wav", "length": 257119, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_7_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M01_F10_K004_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M01_F10_K004_14_channel3.wav", "length": 260586, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M01_F10_K004_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI21_N15_M07_F10_KI21_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI21_N15_M07_F10_KI21_19_channel2.wav", "length": 256092, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI21_N15_M07_F10_KI21_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_6_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F10_KI18_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F10_KI18_18_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F10_KI18_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N15_M01_F10_K005_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N15_M01_F10_K005_16_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N15_M01_F10_K005_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_3_channel3.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_6_channel2.wav", "length": 256074, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_18_channel3.wav", "length": 256082, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_1_channel3.wav", "length": 256070, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N15_M07_F04_KI08_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N15_M07_F04_KI08_2_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N15_M07_F04_KI08_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI01_N15_M07_F04_KI01_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI01_N15_M07_F04_KI01_2_channel2.wav", "length": 256942, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI01_N15_M07_F04_KI01_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F10_KB23_6_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F10_KB23_6_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F10_KB23_6_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_12_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_15_channel3.wav", "length": 256096, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_6_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_6_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_6_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_10_channel2.wav", "length": 256078, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_19_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_19_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_19_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_13_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_14_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_14_channel2.wav", "length": 256097, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_14_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_19_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_9_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_9_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_9_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M07_F10_KA09_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M07_F10_KA09_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M07_F10_KA09_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K006_N15_M07_F04_K006_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K006_N15_M07_F04_K006_18_channel2.wav", "length": 260797, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K006_N15_M07_F04_K006_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_2_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_20_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_16_channel2.wav", "length": 256087, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N15_M07_F10_KB24_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N15_M07_F10_KB24_12_channel2.wav", "length": 256287, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N15_M07_F10_KB24_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA15_N15_M01_F10_KA15_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA15_N15_M01_F10_KA15_10_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA15_N15_M01_F10_KA15_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F10_KB23_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F10_KB23_14_channel3.wav", "length": 256088, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F10_KB23_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_12_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_10_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_5_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_5_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_5_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_2_channel3.wav", "length": 259003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M01_F10_KA09_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M01_F10_KA09_2_channel2.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M01_F10_KA09_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_11_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_11_channel2.wav", "length": 257449, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_11_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA30_N15_M07_F04_KA30_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA30_N15_M07_F04_KA30_5_channel2.wav", "length": 256084, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA30_N15_M07_F04_KA30_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N15_M07_F10_K004_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N15_M07_F10_K004_1_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N15_M07_F10_K004_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M07_F10_KA04_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M07_F10_KA04_20_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M07_F10_KA04_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI17_N15_M07_F10_KI17_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI17_N15_M07_F10_KI17_20_channel2.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI17_N15_M07_F10_KI17_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA22_N15_M01_F10_KA22_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA22_N15_M01_F10_KA22_2_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA22_N15_M01_F10_KA22_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N09_M07_F10_KA04_9_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N09_M07_F10_KA04_9_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N09_M07_F10_KA04_9_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N15_M07_F04_KI18_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N15_M07_F04_KI18_1_channel3.wav", "length": 277460, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N15_M07_F04_KI18_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_1_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_1_channel3.wav", "length": 263829, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_1_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F10_KA05_16_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F10_KA05_16_channel2.wav", "length": 250604, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F10_KA05_16_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_5_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_5_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_5_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_13_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_17_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_17_channel3.wav", "length": 256005, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_17_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F04_KB23_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F04_KB23_20_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F04_KB23_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K002_N15_M07_F10_K002_8_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K002_N15_M07_F10_K002_8_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K002_N15_M07_F10_K002_8_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_2_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N09_M07_F10_KI08_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N09_M07_F10_KI08_4_channel3.wav", "length": 255999, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N09_M07_F10_KI08_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI07_N15_M07_F10_KI07_10_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI07_N15_M07_F10_KI07_10_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI07_N15_M07_F10_KI07_10_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_2_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K005_N09_M07_F10_K005_10_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K005_N09_M07_F10_K005_10_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K005_N09_M07_F10_K005_10_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M07_F10_KA01_3_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M07_F10_KA01_3_channel3.wav", "length": 256003, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M07_F10_KA01_3_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K004_N09_M07_F10_K004_4_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K004_N09_M07_F10_K004_4_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K004_N09_M07_F10_K004_4_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N15_M07_F04_KA05_8_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N15_M07_F04_KA05_8_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N15_M07_F04_KA05_8_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_18_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_18_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_18_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA06_N15_M07_F10_KA06_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA06_N15_M07_F10_KA06_1_channel2.wav", "length": 256083, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA06_N15_M07_F10_KA06_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI05_N09_M07_F10_KI05_12_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI05_N09_M07_F10_KI05_12_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI05_N09_M07_F10_KI05_12_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_15_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_15_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_15_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB23_N15_M07_F10_KB23_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB23_N15_M07_F10_KB23_4_channel3.wav", "length": 256000, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB23_N15_M07_F10_KB23_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KB24_N09_M07_F10_KB24_4_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KB24_N09_M07_F10_KB24_4_channel3.wav", "length": 257468, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KB24_N09_M07_F10_KB24_4_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA30_N09_M07_F10_KA30_13_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA30_N09_M07_F10_KA30_13_channel2.wav", "length": 256004, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA30_N09_M07_F10_KA30_13_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA08_N15_M01_F10_KA08_14_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA08_N15_M01_F10_KA08_14_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA08_N15_M01_F10_KA08_14_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI16_N09_M07_F10_KI16_2_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI16_N09_M07_F10_KI16_2_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI16_N09_M07_F10_KI16_2_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_artificial_KI08_N09_M07_F10_KI08_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_artificial_KI08_N09_M07_F10_KI08_12_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_artificial_KI08_N09_M07_F10_KI08_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KA04_N15_M01_F10_KA04_5_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KA04_N15_M01_F10_KA04_5_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KA04_N15_M01_F10_KA04_5_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA01_N15_M01_F10_KA01_2_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA01_N15_M01_F10_KA01_2_channel3.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA01_N15_M01_F10_KA01_2_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K003_N15_M07_F10_K003_12_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K003_N15_M07_F10_K003_12_channel3.wav", "length": 256171, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K003_N15_M07_F10_K003_12_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_lifetime_KB27_N15_M01_F10_KB27_20_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_lifetime_KB27_N15_M01_F10_KB27_20_channel2.wav", "length": 256090, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_lifetime_KB27_N15_M01_F10_KB27_20_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI18_N09_M07_F10_KI18_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI18_N09_M07_F10_KI18_1_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI18_N09_M07_F10_KI18_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA05_N09_M07_F10_KA05_1_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA05_N09_M07_F10_KA05_1_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA05_N09_M07_F10_KA05_1_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI04_N15_M07_F04_KI04_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI04_N15_M07_F04_KI04_13_channel3.wav", "length": 256091, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI04_N15_M07_F04_KI04_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M07_F04_K001_13_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M07_F04_K001_13_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M07_F04_K001_13_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA09_N15_M01_F10_KA09_3_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA09_N15_M01_F10_KA09_3_channel2.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA09_N15_M01_F10_KA09_3_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "OR_artificial_KA03_N09_M07_F10_KA03_20_channel3", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/OR_artificial_KA03_N09_M07_F10_KA03_20_channel3.wav", "length": 256001, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: OR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["OR"]}, "meta_data": {"original_filename": "OR_artificial_KA03_N09_M07_F10_KA03_20_channel3.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "IR_lifetime_KI14_N15_M07_F10_KI14_19_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/IR_lifetime_KI14_N15_M07_F10_KI14_19_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: IR", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["IR"]}, "meta_data": {"original_filename": "IR_lifetime_KI14_N15_M07_F10_KI14_19_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}
{"task_name": "PU_cur", "dataset_name": "PU", "id": "healthy_K001_N15_M01_F10_K001_18_channel2", "data_type": "wav", "input_ts": {"num_channel": 1, "channel_detail": [""], "path": "Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/raw_input/healthy_K001_N15_M01_F10_K001_18_channel2.wav", "length": 256002, "timestamps": [], "fs": 64000}, "input_text": "Now, I need your assistance to complete a fault classification task. This is a bearing fault diagnosis task based on motor current signals. It is known that the provided motor current signal sample corresponds to one of the following categories: healthy, IR, OR. Please identify the classification result represented by this data.", "gt_text": "The classification result is: healthy", "gt_result": {"class_list": ["healthy", "IR", "OR"], "gt_class": ["healthy"]}, "meta_data": {"original_filename": "healthy_K001_N15_M01_F10_K001_18_channel2.wav", "file_format": ".wav", "sample_width": 2, "n_channels": 1}}