{"id":"W2399977860","doi":"","title":"TAC 2008 Question Answering Experiments at Tokyo Institute of Technology.","year":2008,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Question answering; Information retrieval","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":[0.004564647,0.0009946461,0.001286379,0.001059197,0.001856612,0.001871238,0.001767774,0.002307654,0.0294232],"category_scores_gemma":[0.01136377,0.0005277005,0.0005618366,0.001140098,0.000531795,0.003757498,0.001353518,0.002540193,0.01055264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075007,"about_ca_system_score_gemma":0.001642651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008386229,"about_ca_topic_score_gemma":0.0102646,"domain_scores_codex":[0.9970867,0.001320959,0.0001823243,0.0007307388,0.000461429,0.0002178656],"domain_scores_gemma":[0.9907584,0.004182876,0.0002068108,0.001482632,0.0024772,0.0008920644],"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":[0.003832119,0.004927693,0.004919339,0.0006713272,0.0001825549,0.0004810401,0.001746466,0.001685984,0.0235456,0.005908508,0.8289604,0.1231391],"study_design_scores_gemma":[0.006643027,0.00469081,0.05355418,0.0001842798,0.0005766915,0.001233552,0.001994028,0.06103017,0.04113661,0.01969967,0.8088964,0.0003606916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5665834,0.003008938,0.07081395,0.01598474,0.00577064,0.00662307,0.1221759,0.03891582,0.1701235],"genre_scores_gemma":[0.6259341,0.0006615081,0.08931221,0.003026937,0.001083168,0.006859988,0.1821342,0.002126936,0.08886108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0294232,"threshold_uncertainty_score":0.09843045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01433929541817701,"score_gpt":0.2598021874426596,"score_spread":0.2454628920244826,"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."}}