{"id":"W2805607905","doi":"","title":"UZH at TAC KBP 2017: Event Nugget Detection via Joint Learning with Softmax-Margin Objective.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Softmax function; Margin (machine learning); Joint (building); Computer science; Event (particle physics); Artificial intelligence; Machine learning; Deep learning; Engineering; Physics; Structural engineering","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":[0.005145232,0.002965295,0.003959147,0.001845442,0.001477116,0.003403262,0.002914539,0.00385834,0.01551918],"category_scores_gemma":[0.01643766,0.001196343,0.001556325,0.002303021,0.00114594,0.004668708,0.004242808,0.006792116,0.009987568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001361707,"about_ca_system_score_gemma":0.002152551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01372598,"about_ca_topic_score_gemma":0.01803627,"domain_scores_codex":[0.9976256,0.0009807833,0.00009193993,0.0004857999,0.0005908185,0.0002249784],"domain_scores_gemma":[0.9957986,0.002340835,0.00008168389,0.0006208789,0.000731862,0.0004261431],"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":[0.002445174,0.0004340973,0.001025174,0.0004401217,0.0004162373,0.0004288646,0.0002723152,0.05554109,0.004294149,0.01311959,0.6643685,0.2572147],"study_design_scores_gemma":[0.000442088,0.0001592221,0.0007825227,0.00006518194,0.00007901484,0.0001240056,0.0001388205,0.9054771,0.00482179,0.05365622,0.03418095,0.00007310013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03765368,0.007716495,0.8507183,0.01358867,0.01201079,0.0005418775,0.02014104,0.04246541,0.01516382],"genre_scores_gemma":[0.3125123,0.001784275,0.5473371,0.003311656,0.004905572,0.000803974,0.06484639,0.007081206,0.05741751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01551918,"threshold_uncertainty_score":0.05191678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01043420943244808,"score_gpt":0.2388014463683573,"score_spread":0.2283672369359092,"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."}}