{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009419945,0.00009535611,0.0001309064,0.0000419231,0.0009652824,0.00003123,0.0003057534,0.000116182,0.00001954318],"category_scores_gemma":[0.0001916448,0.00008335093,0.00004072883,0.00004613528,0.001018319,0.00001110713,0.0002076257,0.0001286744,0.00002576313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001676822,"about_ca_system_score_gemma":0.00004122247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003001658,"about_ca_topic_score_gemma":0.0000915756,"domain_scores_codex":[0.9991037,0.0001517195,0.0001825902,0.0002769168,0.0001286319,0.0001563928],"domain_scores_gemma":[0.998684,0.0001829967,0.0001657942,0.0007583288,0.0001389488,0.00006994619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008636593,0.0002609118,0.002729365,0.0002882058,0.0002463988,0.000004370278,0.00131448,0.000007282035,0.5099719,0.4111087,0.02014217,0.05306262],"study_design_scores_gemma":[0.0006330662,0.00007554147,0.008475619,0.00006590751,0.00007570485,0.0000227015,0.01063191,0.00001352561,0.3489034,0.04286183,0.5879488,0.0002919826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757609,0.002946397,0.003752754,0.0006330512,0.000160863,0.0003430003,0.00014801,0.00005262751,0.01620245],"genre_scores_gemma":[0.9905359,0.0005075164,0.0003935038,0.00001144352,0.0002309577,0.000156366,0.0001131964,0.000009607104,0.008041487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5678067,"threshold_uncertainty_score":0.7424269,"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."}}