{"id":"W2408224326","doi":"","title":"Biological event extraction using subgraph matching.","year":2010,"lang":"en","type":"article","venue":"Semantic Mining in Biomedicine","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Subgraph isomorphism problem; Dependency (UML); Event (particle physics); Task (project management); Matching (statistics); Dependency graph; Graph; Information extraction; Relationship extraction; Biomedical text mining; Artificial intelligence; Data mining; Natural language processing; Theoretical computer science; Text mining; Mathematics","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":[0.001323024,0.00113341,0.0009287816,0.009508621,0.0007851128,0.001118909,0.001340234,0.001262353,0.004278003],"category_scores_gemma":[0.007226815,0.0004473559,0.002408103,0.006756513,0.0004700967,0.002322682,0.001749906,0.0008078442,0.002575233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007807747,"about_ca_system_score_gemma":0.001691149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004132671,"about_ca_topic_score_gemma":0.006005153,"domain_scores_codex":[0.9982505,0.0003620673,0.0002469676,0.0005404396,0.0004964208,0.0001035408],"domain_scores_gemma":[0.9968207,0.001592448,0.0004618174,0.0005502444,0.0004729436,0.0001018833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006119609,0.0004783515,0.01494055,0.004168288,0.0009969083,0.002546842,0.0008077213,0.01872155,0.07361955,0.04130701,0.05834934,0.783452],"study_design_scores_gemma":[0.0002319622,0.0004134194,0.02344419,0.0005187138,0.001169192,0.006247746,0.0008665508,0.3778743,0.1043531,0.2794462,0.2052661,0.0001685362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03395268,0.002341697,0.9063677,0.0009244261,0.0002183826,0.001416978,0.02898363,0.01825578,0.007538699],"genre_scores_gemma":[0.1623051,0.001587411,0.7449926,0.0003467571,0.0001396451,0.0007505169,0.08567498,0.0007937104,0.003409244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009508621,"threshold_uncertainty_score":0.01431137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515216713681953,"score_gpt":0.3264374321826622,"score_spread":0.3012852650458427,"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."}}