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items/README_items.md
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# items/ — the full item pool after step 0
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`pool_items_base.parquet` (717,199 rows, one per question of 139 video benchmarks) columns:
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benchmark, item_id (pipeline qid; unique), video_key (source reference), video_id (content hash of the
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normalized video, or "k:<sha1-16>" when the video was not normalized at snapshot time), video_path
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(cluster path, not usable off-cluster; frames are read from the frame cache instead), question,
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question_raw, options (list of option strings, letter prefixes stripped), options_source,
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option_prefix_stripped, n_options, options_raw, answer (gold letter), answer_idx, duration (s),
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declared_task, declared_scene, license, format (mcq / open / ...), format_reason,
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bench_question_format, in_sample (item belongs to the 300-item E1 sample).
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`step0_kept.parquet` (717,199 rows): item_id, format, kept (False = removed in step 0),
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removed_reason, dup_of, dup_cos. Steps 1-3 run on rows with kept == True and format == "mcq"
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(299,366 items); step 2 and 3 additionally need a frame cache entry for the item's video_id.
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`step0_removed.jsonl`: the removed rows with reasons; `screen_counts_full.csv`: per-benchmark counts;
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`pool_schema_manifest.csv`: how each benchmark was enumerated.
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