{"id":"W2807195765","doi":"","title":"CMU CS Event TAC-KBP2016 Event Argument Extraction System.","year":2016,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Argument (complex analysis); Event (particle physics); Computer science; Extraction (chemistry); Real-time computing; Chemistry; Chromatography; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001989714,0.002046693,0.001027612,0.005616089,0.001044395,0.002370404,0.0021169,0.001384328,0.06513788],"category_scores_gemma":[0.01157946,0.000679283,0.0008717079,0.003278058,0.0002964892,0.004330727,0.002579948,0.001531085,0.05410638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009875172,"about_ca_system_score_gemma":0.00215553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007336302,"about_ca_topic_score_gemma":0.01009674,"domain_scores_codex":[0.9985352,0.000394595,0.0001750764,0.0003499616,0.0004519294,0.0000931664],"domain_scores_gemma":[0.9961391,0.001520368,0.0002641032,0.0007809896,0.001043437,0.000251936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006209576,0.0001595977,0.001214093,0.00109276,0.00008946101,0.0003027964,0.0003325354,0.001693517,0.004389271,0.006872486,0.8829272,0.1003054],"study_design_scores_gemma":[0.0004627536,0.0001129435,0.003737102,0.0002458322,0.0001262126,0.0005209472,0.0003897915,0.07430635,0.01674477,0.01773861,0.8854916,0.0001230689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01036137,0.0007120782,0.1715578,0.001264384,0.0006836106,0.001027225,0.4322127,0.3257006,0.0564801],"genre_scores_gemma":[0.04386741,0.0003576278,0.200904,0.0004463596,0.0003055375,0.001106479,0.7177376,0.01299068,0.0222843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06513788,"threshold_uncertainty_score":0.2179079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009045819101575923,"score_gpt":0.2550209852932606,"score_spread":0.2459751661916846,"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."}}