{"id":"W2003601914","doi":"10.1186/1471-2105-13-s11-s7","title":"Biological event composition","year":2012,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Coreference; Biomedical text mining; Event (particle physics); Natural language processing; Task (project management); Artificial intelligence; Parsing; Information extraction; Resolution (logic); Information retrieval; Text mining","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":[0.0002189455,0.00009805423,0.00009356922,0.0000214165,0.00006073408,0.00001165544,0.0001171508,0.0001845571,0.00002998812],"category_scores_gemma":[0.00007789492,0.00007156815,0.0000675462,0.0000445704,0.0001059161,0.000004148035,0.0000915753,0.00006129049,0.0001441086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008568582,"about_ca_system_score_gemma":0.00002028881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001062155,"about_ca_topic_score_gemma":6.015897e-7,"domain_scores_codex":[0.9993382,0.0000283413,0.0002059746,0.00006873654,0.00009250716,0.0002663004],"domain_scores_gemma":[0.9996119,0.00001706417,0.00006460755,0.000174841,0.00002206602,0.0001095919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005301455,0.001566837,0.4601416,0.0005101601,0.0002924477,0.000002597449,0.002231357,0.0002663135,0.07811535,0.005305118,0.1209844,0.3300537],"study_design_scores_gemma":[0.002125627,0.001423277,0.1687427,0.0000737537,0.00005814025,0.0002433356,0.001991188,0.01306187,0.04704756,0.0002117536,0.7638083,0.001212556],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6231728,0.001210636,0.3696668,0.0001174818,0.0005776305,0.0001877244,0.00002389979,0.0000858409,0.004957186],"genre_scores_gemma":[0.8636115,0.00007549082,0.1351831,0.0004665497,0.0003199756,0.00001130906,0.0001977511,0.000006469544,0.000127915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6428239,"threshold_uncertainty_score":0.2918465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03703801914562316,"score_gpt":0.2905017331203857,"score_spread":0.2534637139747626,"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."}}