{"id":"W2805550868","doi":"","title":"New York University 2016 System for KBP Event Nugget: A Deep Learning Approach.","year":2016,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Computer science; Artificial intelligence; History; 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":[0.001353609,0.001180251,0.00107699,0.002675449,0.0007590844,0.001772971,0.002011681,0.001219291,0.04720232],"category_scores_gemma":[0.007068744,0.0006810449,0.0006208201,0.001847621,0.0002869173,0.004543143,0.002889184,0.001954463,0.03226892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095576,"about_ca_system_score_gemma":0.0018895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01925131,"about_ca_topic_score_gemma":0.0248906,"domain_scores_codex":[0.9993531,0.0001045947,0.00006438667,0.0002341434,0.0001756691,0.000068092],"domain_scores_gemma":[0.9983963,0.0004139844,0.00008636872,0.0004861738,0.0004436574,0.0001734346],"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":[0.00111539,0.0002272447,0.00364148,0.0006237745,0.0001598543,0.0002513139,0.0003556802,0.005111031,0.004567222,0.006199553,0.759908,0.2178394],"study_design_scores_gemma":[0.0005481301,0.0002485089,0.006497366,0.0002470363,0.0001730219,0.0003923448,0.0003073736,0.3394852,0.02343043,0.0270597,0.6014147,0.0001961856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.02563921,0.001573637,0.1796959,0.001988669,0.001267249,0.001014416,0.2385934,0.5115215,0.038706],"genre_scores_gemma":[0.1554271,0.001040607,0.2577577,0.0007642661,0.0003841404,0.001242931,0.5027919,0.01389555,0.06669586],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04720232,"threshold_uncertainty_score":0.1579075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151717209770329,"score_gpt":0.2096102176407464,"score_spread":0.1980930455430431,"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."}}