{"id":"W2132630274","doi":"","title":"York University at TREC 2012: Medical Records Track","year":2012,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Track (disk drive); Computer science; Information retrieval; Medical record; Work (physics); Artificial intelligence; Medicine; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02224003,0.004561493,0.004415937,0.009036218,0.004073566,0.00575712,0.004554925,0.004161833,0.05565176],"category_scores_gemma":[0.0326659,0.001126923,0.001867051,0.008430782,0.001197868,0.006674117,0.003105791,0.004708813,0.04642735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006942063,"about_ca_system_score_gemma":0.01171594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1027113,"about_ca_topic_score_gemma":0.1540458,"domain_scores_codex":[0.9862541,0.004077873,0.001257223,0.001399558,0.005976236,0.001035001],"domain_scores_gemma":[0.9615148,0.006752898,0.002158585,0.005678092,0.01964117,0.004254486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002064366,0.0001683102,0.000442349,0.0005055803,0.00005970492,0.00003418656,0.00002884901,0.0003923673,0.001060528,0.0002931753,0.980867,0.0159415],"study_design_scores_gemma":[0.001630395,0.00104166,0.02624767,0.0006141818,0.0003355977,0.0004396406,0.0003129735,0.01850154,0.009926459,0.003230595,0.937322,0.0003972358],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01646362,0.01545749,0.01115247,0.0227003,0.008755158,0.003727584,0.846627,0.02893306,0.0461833],"genre_scores_gemma":[0.01209203,0.002769531,0.01424167,0.002364589,0.001188213,0.001627346,0.9285212,0.001002668,0.03619266],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1027113,"threshold_uncertainty_score":0.2042269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03818721144490055,"score_gpt":0.2545070467609054,"score_spread":0.2163198353160048,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}