{"id":"W2806375004","doi":"","title":"Modeling Event Extraction via Multilingual Data Sources.","year":2015,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Event data; Extraction (chemistry); Event (particle physics); Data extraction; Natural language processing; Information extraction; Chromatography; Chemistry; Physics; MEDLINE","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008998631,0.00006725813,0.00008908888,0.00004405191,0.00008699798,0.00003597278,0.0006168965,0.00003321137,0.000001694977],"category_scores_gemma":[0.00004257557,0.00006275836,0.00001181347,0.0001061689,0.00005851194,0.0004321061,0.0003042502,0.00006629269,0.000004683269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008410429,"about_ca_system_score_gemma":0.00005364655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004445732,"about_ca_topic_score_gemma":0.000004757267,"domain_scores_codex":[0.9993047,0.00004768606,0.0001866412,0.0002493163,0.0001211067,0.00009056801],"domain_scores_gemma":[0.9988734,0.00007477414,0.00006097808,0.0008395187,0.00009194075,0.00005937239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001303747,0.00004413648,0.00003983546,0.00001717111,0.000009959145,2.226633e-7,0.001784024,0.02275711,0.0004817267,0.7834058,0.000007206203,0.1914397],"study_design_scores_gemma":[0.0001051372,0.000008419229,0.000005833741,0.000003157075,0.00000842148,0.000007833166,0.0005935316,0.6233588,0.001915871,0.3729515,0.0009710006,0.00007046419],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05406824,0.0006666696,0.9446681,0.0000898106,0.00004310177,0.0001198545,0.000003067959,0.00006541524,0.0002757327],"genre_scores_gemma":[0.9803008,0.00001887849,0.01945788,0.00002080642,0.00008160964,0.00002489451,0.0000129826,0.000004222315,0.00007790293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9262326,"threshold_uncertainty_score":0.2559212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04752959636667484,"score_gpt":0.3167967502811873,"score_spread":0.2692671539145124,"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."}}