{"id":"W2806332557","doi":"","title":"Events Detection, Coreference and Sequencing: What's next? Overview of the TAC KBP 2017 Event Track.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Coreference; Track (disk drive); Computer science; Event (particle physics); Artificial intelligence; Resolution (logic); Operating system","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":[0.01359436,0.001584858,0.001614928,0.01197281,0.002507439,0.005608699,0.003484621,0.002230817,0.008933191],"category_scores_gemma":[0.02454815,0.001190206,0.001055378,0.01218024,0.0009743989,0.00997363,0.00423557,0.003816125,0.009335927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002432529,"about_ca_system_score_gemma":0.009564701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03868213,"about_ca_topic_score_gemma":0.04996977,"domain_scores_codex":[0.9939631,0.00118882,0.0006228457,0.001001871,0.002833989,0.0003892615],"domain_scores_gemma":[0.9776372,0.007217624,0.001161476,0.00288613,0.01008383,0.00101375],"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":[0.0003831857,0.0002305955,0.005946228,0.001749364,0.0001645239,0.0002031241,0.001137637,0.00392292,0.007411066,0.0101355,0.2720789,0.696637],"study_design_scores_gemma":[0.0001218983,0.0002637059,0.01044234,0.001192037,0.000281999,0.0008230047,0.0009666756,0.04436619,0.01972721,0.03284436,0.888752,0.0002187548],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02359006,0.04096876,0.7658702,0.010958,0.003045436,0.001611343,0.0594487,0.05139479,0.04311284],"genre_scores_gemma":[0.07669095,0.02324069,0.5317926,0.002955762,0.001519941,0.002454059,0.3177264,0.008066282,0.03555334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03868213,"threshold_uncertainty_score":0.07691395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05645302311602707,"score_gpt":0.3019632715515778,"score_spread":0.2455102484355507,"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."}}