{"id":"W2189229433","doi":"","title":"York University at TREC 2011: Medical Records Track","year":2011,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Query expansion; Information retrieval; Unified Medical Language System; Search engine indexing; Ranking (information retrieval); Matching (statistics); Relevance feedback; Relevance (law); Weighting; Semantic matching; Web search query; Query language; Image retrieval; Search engine; Artificial intelligence; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001524212,0.00009004406,0.00009865094,0.00002093831,0.00006420659,0.000002351814,0.0002519923,0.0003104011,0.004631035],"category_scores_gemma":[0.00006965325,0.00007332921,0.00007075374,0.00003920171,0.0002166695,9.113968e-7,0.0001464882,0.00007751283,0.0002211318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001230746,"about_ca_system_score_gemma":0.00006165279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001895551,"about_ca_topic_score_gemma":0.000514521,"domain_scores_codex":[0.9992937,0.00004621443,0.0000937092,0.0002497523,0.000125959,0.0001906565],"domain_scores_gemma":[0.9995467,0.00001233341,0.00002851163,0.000217106,0.00001960437,0.0001757381],"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":[0.0007487989,0.0003997821,0.03397474,0.00002338133,0.000190812,0.0001479307,0.0004494245,1.12989e-7,0.01618852,0.0007351688,0.6594081,0.2877333],"study_design_scores_gemma":[0.0006629198,0.0004495106,0.01990197,0.000007704989,0.00001748599,0.00004669133,0.0003564026,0.00002009145,0.02406481,0.00006561475,0.9541796,0.000227202],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8362076,0.0004306055,0.001568023,0.0005463287,0.000375213,0.0000601617,0.000009888192,0.00008295254,0.1607192],"genre_scores_gemma":[0.9141206,0.0004204162,0.006993797,0.0006097351,0.0002009303,0.000001214885,0.00004584808,0.00001318799,0.07759432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2947715,"threshold_uncertainty_score":0.9962789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03467695990857757,"score_gpt":0.2325296340621002,"score_spread":0.1978526741535226,"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."}}