{"id":"W2970640551","doi":"","title":"Overview of the TAC 2018 Drug-Drug Interaction Extraction from Drug Labels Track.","year":2018,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Drug; Drug-drug interaction; Computer science; Track (disk drive); 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":[0.002899135,0.001451991,0.001502659,0.01130572,0.001437729,0.003526557,0.002406558,0.001462394,0.01191591],"category_scores_gemma":[0.009421013,0.0006541194,0.001978549,0.007841637,0.0004492187,0.003620025,0.002535718,0.001839787,0.01483318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001632522,"about_ca_system_score_gemma":0.005629475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02132061,"about_ca_topic_score_gemma":0.03163631,"domain_scores_codex":[0.9975242,0.0003839694,0.0003195275,0.0005616362,0.001012905,0.0001977742],"domain_scores_gemma":[0.9956973,0.001025102,0.0002752878,0.001108152,0.001594613,0.0002996803],"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":[0.0004696182,0.0002829407,0.004891409,0.002296337,0.0003839915,0.0003095187,0.0001801105,0.00484104,0.009893735,0.01117947,0.5391916,0.4260802],"study_design_scores_gemma":[0.0001413293,0.0002654331,0.00887056,0.0006898855,0.0004077065,0.0008227946,0.0001808464,0.08197521,0.01519843,0.02426168,0.8670622,0.0001239879],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01322458,0.01756476,0.4605446,0.003831383,0.001525274,0.001755784,0.3193325,0.1413527,0.04086839],"genre_scores_gemma":[0.02947368,0.004510313,0.3428273,0.001101154,0.0003370577,0.001146144,0.6064379,0.002738635,0.01142786],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02132061,"threshold_uncertainty_score":0.04239303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01562576115517499,"score_gpt":0.3036536047877693,"score_spread":0.2880278436325943,"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."}}