Add GitHub ML run 20260925T215221000000Z-f0fe8426
Browse files
coverage/20260925T215221000000Z-f0fe8426.json
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{
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"complete_sweep": false,
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"date_field": "created",
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"end": "2026-09-24",
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"mode": "historical-sample",
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"queries": [
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{
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"coverage_gap": true,
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"coverage_gap_reason": "first_page_sample_only",
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"incomplete_results": false,
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"pages_scanned": 1,
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"query": "\"carbon footprint\" \"deep learning\" training in:description,readme created:2026-01-01..2026-09-24",
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"query_id": "specialized.carbon-footprint-training",
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"repositories_observed_on_last_page": 100,
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"sampled_first_page": true,
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"search_limit_reached": false,
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"status": "sampled",
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"total_count": 390,
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"year": 2026
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}
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],
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"requests_used": 1,
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"run_id": "20260925T215221000000Z-f0fe8426",
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"start_year": 2008,
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"started_at": "2026-09-25T21:52:21Z"
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}
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data/observations/2026/09/25/20260925T215221000000Z-f0fe8426.jsonl
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| 9 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-01-24T18:12:19Z","description":"\"Hybrid Deep Learning Model for Smart Grid Load Forecasting and Anomaly Detection\"","domains":["green-ai","sustainable-computing","time-series-and-forecasting"],"fork":false,"forks":0,"github_id":1141362564,"homepage":null,"language":"Jupyter Notebook","license":null,"methods":["carbon-accounting","training-efficiency"],"name":"suryaa-ai/energrid-ai-smart-grid","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-25T21:52:21Z","pushed_at":"2026-06-20T20:22:48Z","query_ids":["specialized.carbon-footprint-training"],"stars":0,"topics":[],"updated_at":"2026-06-20T20:22:53Z","url":"https://github.com/suryaa-ai/energrid-ai-smart-grid"}
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{"archived":false,"candidate_status":"candidate","created_at":"2026-01-27T09:31:32Z","description":"An intelligent energy monitoring system using ML models (LSTM, Random Forest, ARIMA) to predict 24-hour power usage, detect anomalies, and optimize consumption. Features real-time dashboards, appliance-level insights, and one-click PDF reports for smart homes and industries.","domains":["green-ai","sustainable-computing"],"fork":false,"forks":0,"github_id":1143197145,"homepage":null,"language":null,"license":null,"methods":["carbon-accounting","random-forest","training-efficiency"],"name":"deepakparagi/Ai-Based-Energy-consumption-prediction","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-25T21:52:21Z","pushed_at":"2026-09-04T05:35:55Z","query_ids":["specialized.carbon-footprint-training"],"stars":0,"topics":[],"updated_at":"2026-09-04T05:36:01Z","url":"https://github.com/deepakparagi/Ai-Based-Energy-consumption-prediction"}
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| 13 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-01-30T12:02:50Z","description":"Implemented fine-tuned RoBERTa and DistilRoBERTa models to analyse consumer sentiment for the L'Oréal Brandstorm Hackathon. Used transfer learning and deep learning to categorize large-scale text data into actionable marketing insights.","domains":["green-ai","natural-language-processing","sustainable-computing"],"fork":false,"forks":2,"github_id":1145933848,"homepage":"https://colab.research.google.com/drive/1oSUkmuNAqj1v-nHMs_vhYHIZO6yxxPiV?usp=drive_link","language":"Jupyter Notebook","license":null,"methods":["carbon-accounting","training-efficiency"],"name":"ShubhamShirsekar/L-Oreal-Hackathon","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-25T21:52:21Z","pushed_at":"2026-01-30T12:46:08Z","query_ids":["specialized.carbon-footprint-training"],"stars":0,"topics":["bert","bert-fine-tuning","machine-learning","nlp","python","text-classification"],"updated_at":"2026-03-14T15:06:09Z","url":"https://github.com/ShubhamShirsekar/L-Oreal-Hackathon"}
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{"archived":false,"candidate_status":"candidate","created_at":"2026-02-17T10:48:03Z","description":"Energy-efficient sentiment analysis pipeline with TinyBERT and INT8 quantization (CPU-only deployment).","domains":["green-ai","machine-learning-systems","natural-language-processing","sustainable-computing"],"fork":false,"forks":0,"github_id":1159935682,"homepage":null,"language":"Python","license":null,"methods":["carbon-accounting","quantization","training-efficiency"],"name":"supu02/green-ai-efficient-nlp","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-25T21:52:21Z","pushed_at":"2026-02-17T16:55:17Z","query_ids":["specialized.carbon-footprint-training"],"stars":0,"topics":[],"updated_at":"2026-02-17T16:59:03Z","url":"https://github.com/supu02/green-ai-efficient-nlp"}
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| 100 |
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|
state/historical-sample.json
CHANGED
|
@@ -3428,6 +3428,12 @@
|
|
| 3428 |
2026
|
| 3429 |
]
|
| 3430 |
},
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3431 |
"timeseries.anomaly-detection": {
|
| 3432 |
"query": "\"time series anomaly detection\" in:description",
|
| 3433 |
"years": [
|
|
@@ -3564,5 +3570,5 @@
|
|
| 3564 |
"end": "2026-09-24",
|
| 3565 |
"search_policy_version": 2,
|
| 3566 |
"start_year": 2008,
|
| 3567 |
-
"updated_at": "2026-09-25T21:
|
| 3568 |
}
|
|
|
|
| 3428 |
2026
|
| 3429 |
]
|
| 3430 |
},
|
| 3431 |
+
"specialized.carbon-footprint-training": {
|
| 3432 |
+
"query": "\"carbon footprint\" \"deep learning\" training in:description,readme",
|
| 3433 |
+
"years": [
|
| 3434 |
+
2026
|
| 3435 |
+
]
|
| 3436 |
+
},
|
| 3437 |
"timeseries.anomaly-detection": {
|
| 3438 |
"query": "\"time series anomaly detection\" in:description",
|
| 3439 |
"years": [
|
|
|
|
| 3570 |
"end": "2026-09-24",
|
| 3571 |
"search_policy_version": 2,
|
| 3572 |
"start_year": 2008,
|
| 3573 |
+
"updated_at": "2026-09-25T21:52:21Z"
|
| 3574 |
}
|