{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002791653,0.002115318,0.0009919921,0.004507674,0.002509282,0.003703268,0.002553814,0.001604769,0.05253129],"category_scores_gemma":[0.007792289,0.0007551303,0.002188383,0.002949953,0.001123278,0.005725375,0.005158997,0.001853649,0.02385406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001533288,"about_ca_system_score_gemma":0.002667423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002744809,"about_ca_topic_score_gemma":0.002735939,"domain_scores_codex":[0.9962991,0.0004777279,0.0005056218,0.001419633,0.001088521,0.0002094996],"domain_scores_gemma":[0.9954095,0.001676575,0.0003266163,0.0009509606,0.001320704,0.0003156383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001481145,0.0003341727,0.007801678,0.00387417,0.0002083952,0.002669124,0.00234665,0.004734585,0.04282884,0.1142728,0.1131895,0.7062589],"study_design_scores_gemma":[0.00009456349,0.0001255109,0.003328191,0.0003757747,0.0002405251,0.002579736,0.0006188991,0.02740377,0.05282119,0.1013887,0.8109006,0.0001225736],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01922345,0.003181987,0.8195171,0.002419419,0.00147209,0.001772693,0.02142203,0.04198286,0.08900823],"genre_scores_gemma":[0.1216464,0.002492975,0.7483739,0.001770287,0.0009804828,0.001108833,0.06608334,0.006235595,0.05130821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05253129,"threshold_uncertainty_score":0.1757346,"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."}}