{"id":"W2805089341","doi":"","title":"IBM Research System at TAC 2017: Adverse Drug Reactions Extraction from Drug Labels.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"IBM; Drug; Computer science; Adverse drug reaction; Drug reaction; Extraction (chemistry); Pharmacology; Medicine; Chemistry; Chromatography; Nanotechnology; Materials science","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":[0.002574354,0.00235378,0.001990849,0.007353097,0.0008900546,0.003009595,0.001887885,0.001635413,0.03095243],"category_scores_gemma":[0.01230178,0.0007917481,0.001623407,0.005535023,0.0003845561,0.003057424,0.002246014,0.001612421,0.03826425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224975,"about_ca_system_score_gemma":0.003522041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009633102,"about_ca_topic_score_gemma":0.008884442,"domain_scores_codex":[0.9982716,0.0003421381,0.0002710743,0.0004985777,0.00048763,0.0001288743],"domain_scores_gemma":[0.9961635,0.001240137,0.0004676408,0.0008599753,0.0009994338,0.0002693828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001065523,0.0002772394,0.004045145,0.002057322,0.0003945646,0.0002889297,0.0002130606,0.002990477,0.00603396,0.004167015,0.8154883,0.1629784],"study_design_scores_gemma":[0.001395083,0.0006377141,0.01634429,0.0007785513,0.0008114447,0.000878665,0.0004221992,0.1415093,0.03029384,0.04619947,0.7604485,0.0002809845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01112359,0.002430216,0.07709188,0.001555653,0.0005428754,0.0008639165,0.5497558,0.3435054,0.01313066],"genre_scores_gemma":[0.03883623,0.001245826,0.149765,0.0005737874,0.0002893525,0.001045728,0.792952,0.00698768,0.008304387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03095243,"threshold_uncertainty_score":0.1035462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02618652209954466,"score_gpt":0.3417986495347466,"score_spread":0.315612127435202,"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."}}