{"id":"W2805780438","doi":"","title":"GWU English TAC-KBP EL Diagnostic Task with Name Mention.","year":2015,"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":"Task (project management); Computer science; Natural language processing; Linguistics; Philosophy; Economics; Management","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":[0.001331517,0.001840563,0.001082572,0.002552383,0.00151474,0.002082084,0.001819632,0.002832532,0.04914157],"category_scores_gemma":[0.01328908,0.0005071599,0.0007839088,0.001926099,0.0005201257,0.005995014,0.003223361,0.001888194,0.03145071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009970141,"about_ca_system_score_gemma":0.001624422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01014445,"about_ca_topic_score_gemma":0.01103943,"domain_scores_codex":[0.9982401,0.000433644,0.0001955227,0.0005751568,0.0003510028,0.0002045308],"domain_scores_gemma":[0.9933233,0.004004804,0.0002192934,0.0007636994,0.00142124,0.0002677457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0011752,0.0002872696,0.003840676,0.0027141,0.0001264006,0.003501318,0.0008295507,0.003618658,0.01132447,0.006096384,0.7812998,0.1851862],"study_design_scores_gemma":[0.0008215667,0.0003597943,0.01735993,0.001268983,0.0003739895,0.008742222,0.006745969,0.1480849,0.06966134,0.0506416,0.6956152,0.0003245118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1535398,0.003812207,0.1711615,0.008369769,0.003690178,0.001676553,0.3800305,0.1059382,0.1717813],"genre_scores_gemma":[0.4326019,0.0006336249,0.09912124,0.002260528,0.0005091002,0.0008262392,0.4210603,0.004179056,0.03880797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04914157,"threshold_uncertainty_score":0.1643949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009584582317296956,"score_gpt":0.2352331223833074,"score_spread":0.2256485400660105,"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."}}