{"id":"W2509885322","doi":"10.1093/database/baw121","title":"BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID","year":2016,"lang":"en","type":"article","venue":"Database","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; Lunenfeld-Tanenbaum Research Institute; Université de Montréal; Institute for Research in Immunology and Cancer","funders":"National Institute of General Medical Sciences; Biotechnology and Biological Sciences Research Council; National Institutes of Health","keywords":"Computer science; Usability; Annotation; Task (project management); Interoperability; Classifier (UML); World Wide Web; Information retrieval; Data curation; Crowdsourcing; Natural language processing; Artificial intelligence; Human–computer interaction","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":[0.0002507749,0.0002144818,0.0002125653,0.00005768818,0.0001086464,0.00002326857,0.000241381,0.0001671251,0.00004899327],"category_scores_gemma":[0.0006980587,0.0001318114,0.00009907167,0.0002019706,0.0003583073,0.000008020858,0.0001222045,0.00005026371,0.00004167664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001998335,"about_ca_system_score_gemma":0.0001679634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001939305,"about_ca_topic_score_gemma":0.00005130734,"domain_scores_codex":[0.9986824,0.00007859593,0.0002371724,0.0005319221,0.0001325207,0.0003374305],"domain_scores_gemma":[0.9989692,0.0001030898,0.0001183155,0.0004827341,0.0001713923,0.0001552958],"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":[0.0003383729,0.0001120545,0.0003541307,0.00003135649,0.0001061756,0.000006595483,0.00003256036,3.428907e-8,0.7945455,0.0003925014,0.165621,0.0384597],"study_design_scores_gemma":[0.000988979,0.0004370785,0.0003984066,0.00005445965,0.0000243887,0.000003404518,0.0001347722,0.000003158843,0.352395,0.00004194397,0.6453107,0.0002076619],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7156446,0.03957897,0.09639426,0.01097311,0.001871158,0.002406108,0.1318078,0.0003018649,0.001022082],"genre_scores_gemma":[0.9203973,0.009502882,0.05124261,0.002601742,0.001793551,0.0007765729,0.009681187,0.0001407253,0.003863402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4796897,"threshold_uncertainty_score":0.5375115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02559781275306474,"score_gpt":0.3119697022994703,"score_spread":0.2863718895464056,"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."}}