{"id":"W2524580445","doi":"","title":"Something Old, Something New: Identifying Knowledge Source in Bio-Events","year":2013,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Open Text (Canada)","funders":"","keywords":"Computer science; Annotation; Event (particle physics); Domain knowledge; Context (archaeology); Knowledge extraction; Sentence; Natural language processing; Domain (mathematical analysis); Bridge (graph theory); Information retrieval; Knowledge-based systems; Information extraction; Artificial intelligence; Data science","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":[0.0004184229,0.0001746799,0.0001917986,0.000146194,0.000109324,0.00007820153,0.0003223382,0.0003184635,0.0001489468],"category_scores_gemma":[0.000422777,0.000150056,0.00009133727,0.0002173367,0.00009506977,0.000009939273,0.0003032713,0.0002293322,0.0002312259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000219704,"about_ca_system_score_gemma":0.00008951301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009493165,"about_ca_topic_score_gemma":0.0001865087,"domain_scores_codex":[0.9986489,0.00009095581,0.0002933911,0.0004109187,0.0001350282,0.0004207937],"domain_scores_gemma":[0.9993929,0.00005463101,0.00006548425,0.0002884542,0.00004132497,0.000157175],"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":[0.00001687362,0.0001568107,0.02632977,0.0000665205,0.00007628099,0.000004047145,0.00193218,0.00002107661,0.4043713,0.000140134,0.0176289,0.5492561],"study_design_scores_gemma":[0.005047527,0.0006259097,0.07340881,0.0004755281,0.00005932555,0.00006693829,0.007781159,0.001930365,0.3532174,0.0081929,0.5470438,0.002150332],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668181,0.002781043,0.02696012,0.0007990306,0.0003737983,0.0001666445,7.356816e-7,0.0000722776,0.002028231],"genre_scores_gemma":[0.9594561,0.0001439599,0.01543593,0.0007296198,0.0003182901,0.00002215589,0.00001953753,0.0000282505,0.02384614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5471057,"threshold_uncertainty_score":0.6119105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02919196385597044,"score_gpt":0.3055341479342584,"score_spread":0.276342184078288,"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."}}