{"id":"W1995296910","doi":"10.1016/j.jbi.2014.10.002","title":"Combining automatic table classification and relationship extraction in extracting anticancer drug–side effect pairs from full-text articles","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Intertek (Canada)","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Advancing Translational Sciences; National Center for Research Resources; Case Western Reserve University; National Cancer Institute; Foundation for Anesthesia Education and Research","keywords":"Anticancer drug; Drug; Computer science; Classifier (UML); Drug discovery; Medicine; Artificial intelligence; Bioinformatics; Pharmacology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.004134303,0.0001265678,0.0002947135,0.0003897066,0.0001106706,0.0002330542,0.0002997211,0.00008593109,0.000009656948],"category_scores_gemma":[0.002395492,0.000104737,0.00005368539,0.0006206726,0.00009767269,0.002060081,0.00008742166,0.0004583492,0.00001076978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001180572,"about_ca_system_score_gemma":0.0001340843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000175422,"about_ca_topic_score_gemma":0.000005135311,"domain_scores_codex":[0.9973795,0.0004209601,0.001220938,0.0001002317,0.0006731209,0.0002052672],"domain_scores_gemma":[0.9927337,0.005872527,0.0009353453,0.0001825895,0.0001069634,0.0001688459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005112571,0.0002221215,0.03174083,0.000227773,0.00005760946,0.00001350024,0.008037106,0.006833812,0.002542855,0.007412483,0.000524293,0.9423365],"study_design_scores_gemma":[0.0006720222,0.0001195703,0.1349365,0.0002592389,0.00001728446,0.00008014631,0.0004512116,0.8557007,0.0002311987,0.007263487,0.0001684341,0.0001001869],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5334873,0.00004229346,0.4653563,0.0006525432,0.0002617294,0.00005856573,6.140068e-7,0.00001965371,0.000121063],"genre_scores_gemma":[0.8301239,0.00001216575,0.1696658,0.0001006816,0.00008281977,0.000002659061,0.000003083679,0.000005380261,0.000003493383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9422363,"threshold_uncertainty_score":0.4271049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02757781544511864,"score_gpt":0.3173055856966529,"score_spread":0.2897277702515343,"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."}}