{"id":"W3158858160","doi":"","title":"Overview of the TAC 2019 Track on Drug-Drug Interaction Extraction from Drug Labels.","year":2019,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Drug; Drug-drug interaction; Track (disk drive); Computer science; Extraction (chemistry); Pharmacology; Medicine; Chemistry; Chromatography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005312374,0.002316255,0.002335975,0.01682509,0.001986034,0.005753601,0.004345372,0.002631225,0.03125702],"category_scores_gemma":[0.01365826,0.0009799233,0.003071232,0.01371499,0.0006050455,0.005824105,0.003672685,0.002475002,0.0310205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002937021,"about_ca_system_score_gemma":0.007428049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02902158,"about_ca_topic_score_gemma":0.03761255,"domain_scores_codex":[0.9959347,0.0006469805,0.0005630876,0.0008052408,0.001792343,0.000257572],"domain_scores_gemma":[0.992157,0.002355465,0.000477592,0.001818019,0.002649735,0.0005422161],"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":[0.0004323626,0.000212771,0.002185167,0.002632156,0.0004501699,0.0002086496,0.0001366662,0.00234979,0.00533113,0.01114726,0.7591237,0.2157902],"study_design_scores_gemma":[0.0001725794,0.0001769747,0.004160817,0.0007703684,0.0003113555,0.0003438113,0.00008130418,0.01673437,0.006813053,0.01215342,0.9581794,0.0001026231],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.008261792,0.01992224,0.2333158,0.004532702,0.001758795,0.001657363,0.5248839,0.1291683,0.07649904],"genre_scores_gemma":[0.008658357,0.004045862,0.1474763,0.001790558,0.0002670905,0.0007602986,0.8198925,0.004015337,0.01309361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03125702,"threshold_uncertainty_score":0.1045651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01089291464740067,"score_gpt":0.285857184977048,"score_spread":0.2749642703296473,"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."}}