{"id":"W2807572306","doi":"","title":"AIPHES-HD system at TAC KBP 2016: Neural Event Trigger Span Detection and Event Type and Realis Disambiguation with Word Embeddings.","year":2016,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Word (group theory); Computer science; Span (engineering); Natural language processing; Artificial intelligence; Linguistics; Physics; Engineering; Philosophy; Astrophysics","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":[0.0009882055,0.002127935,0.00124749,0.001964503,0.0007596318,0.00165499,0.002661954,0.001549505,0.03550948],"category_scores_gemma":[0.004061244,0.0007879535,0.0007106439,0.001253491,0.0003545241,0.004071085,0.002861013,0.001764209,0.03020438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007271982,"about_ca_system_score_gemma":0.001410049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01505616,"about_ca_topic_score_gemma":0.01950099,"domain_scores_codex":[0.9993064,0.0001055979,0.00005546123,0.0003035209,0.0001539049,0.0000750626],"domain_scores_gemma":[0.9988525,0.0002674368,0.00007192131,0.000366205,0.000323547,0.0001184445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002615024,0.0005108956,0.003465589,0.001142231,0.000436941,0.0006628223,0.0004008712,0.008169574,0.02611096,0.002286001,0.6436742,0.3105249],"study_design_scores_gemma":[0.002413173,0.001070138,0.02088227,0.0003710751,0.0006123952,0.0009611169,0.0009556892,0.5228661,0.102597,0.03358687,0.3131534,0.0005307501],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.08737161,0.002314644,0.1359742,0.001129145,0.002478139,0.0009972369,0.2539961,0.4923162,0.02342272],"genre_scores_gemma":[0.2566087,0.0006312348,0.2269668,0.0006973871,0.0005797607,0.001566378,0.4723493,0.01164177,0.02895867],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03550948,"threshold_uncertainty_score":0.1187911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008474831985252954,"score_gpt":0.2336761292935846,"score_spread":0.2252012973083317,"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."}}