{"id":"W2970591538","doi":"","title":"KlickLabs at the TAC 2018 Drug-drug Interaction Extraction from Drug Labels Track.","year":2018,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Drug; Track (disk drive); Drug-drug interaction; Extraction (chemistry); Computer science; Pharmacology; Chemistry; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006268254,0.0001463876,0.0001709192,0.00008183125,0.0004914333,0.00008413834,0.0005976775,0.0000378618,0.00008469866],"category_scores_gemma":[0.00003006689,0.0001103257,0.00005714802,0.0003950965,0.0005709428,0.0008218673,0.0002212189,0.00013349,0.00007311667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004248605,"about_ca_system_score_gemma":0.00002173357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001417134,"about_ca_topic_score_gemma":0.0001994187,"domain_scores_codex":[0.9988865,0.0001586758,0.000290494,0.0003548093,0.0001644787,0.0001450287],"domain_scores_gemma":[0.9980137,0.0006091241,0.0002849955,0.0008736657,0.0001738003,0.0000447254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005431275,0.00008680865,0.0001099862,0.00001008227,0.00005217697,2.359851e-7,0.004112551,0.00000981898,0.0222971,0.8651599,0.004674752,0.1034323],"study_design_scores_gemma":[0.000081916,0.00001354602,0.000260983,0.000008661747,0.00003952781,0.000004277066,0.0008384036,0.0002400805,0.3521688,0.5821642,0.06404427,0.0001353445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1516927,0.000759387,0.8422114,0.001359228,0.0001074149,0.0003711315,0.00001710641,0.0002782802,0.003203427],"genre_scores_gemma":[0.9896669,0.0001851754,0.006006071,0.0001313,0.0001998674,0.0001938083,0.00002189894,0.00001099321,0.003583959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8379743,"threshold_uncertainty_score":0.4498952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007887684036896365,"score_gpt":0.2852766464164186,"score_spread":0.2773889623795223,"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."}}