{"id":"W2507883388","doi":"10.18653/v1/w16-3005","title":"VERSE: Event and Relation Extraction in the BioNLP 2016 Shared Task","year":2016,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Computer science; Relationship extraction; Biomedical text mining; Task (project management); Relation (database); Event (particle physics); Natural language processing; Information retrieval; Data mining; Text mining; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00880685,0.005100827,0.002280544,0.004796052,0.002431807,0.002963563,0.003443701,0.004229502,0.02399032],"category_scores_gemma":[0.02523959,0.001182219,0.002699119,0.003144894,0.000878717,0.006900779,0.006707052,0.003511952,0.01822323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001974174,"about_ca_system_score_gemma":0.004472805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01585253,"about_ca_topic_score_gemma":0.02137038,"domain_scores_codex":[0.991321,0.002341751,0.001136155,0.002808544,0.001833831,0.0005586717],"domain_scores_gemma":[0.9815235,0.01094283,0.0006316404,0.00354844,0.002422216,0.0009314067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002996724,0.001275909,0.007197102,0.004386922,0.000572142,0.002237272,0.00132412,0.01446928,0.03683948,0.006295986,0.570299,0.3521062],"study_design_scores_gemma":[0.001383683,0.000857561,0.01835522,0.0005246184,0.0004387512,0.003003757,0.001687777,0.2328778,0.08436061,0.0248531,0.6311779,0.0004792611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1066188,0.003597417,0.3006875,0.004140239,0.002054444,0.002835558,0.3482047,0.2037577,0.02810365],"genre_scores_gemma":[0.08703724,0.0004968595,0.2277226,0.000552988,0.0002141528,0.001505989,0.6649619,0.006685739,0.01082243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02399032,"threshold_uncertainty_score":0.08025569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444675606016472,"score_gpt":0.2827033617687736,"score_spread":0.2682566057086089,"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."}}