{"id":"W2806702131","doi":"","title":"UFV in the TAC 2015 Cold Start Knowledge Base Population Track.","year":2015,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Cold start (automotive); Base (topology); Population; Knowledge base; Computer science; Artificial intelligence; Mathematics; Demography; Engineering; Operating system; Sociology; Aerospace 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":[0.01948122,0.0007630869,0.001168175,0.006812689,0.002216091,0.006672647,0.00408754,0.002237952,0.017621],"category_scores_gemma":[0.07400408,0.0006504771,0.0007569795,0.005694651,0.0006721985,0.009195987,0.005256539,0.002913142,0.01432906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004525267,"about_ca_system_score_gemma":0.01045804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0957199,"about_ca_topic_score_gemma":0.09786837,"domain_scores_codex":[0.9906784,0.002210622,0.0005636917,0.001426362,0.004491359,0.0006295876],"domain_scores_gemma":[0.9593389,0.01109637,0.001480815,0.008743809,0.01655892,0.002781269],"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":[0.0006821099,0.0004542445,0.01160249,0.0006304511,0.000145641,0.0001220744,0.0008522037,0.0068364,0.002469997,0.0240974,0.56887,0.383237],"study_design_scores_gemma":[0.0001479044,0.0003533231,0.008732746,0.0006974099,0.000100319,0.0001318722,0.0006182576,0.04960281,0.007099897,0.02170145,0.910693,0.0001211182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.07031358,0.00869688,0.3198362,0.01703165,0.003923163,0.002741384,0.3431463,0.07415797,0.1601529],"genre_scores_gemma":[0.1223372,0.001676957,0.2262338,0.002502949,0.0003664742,0.001464932,0.5974634,0.004509806,0.04344446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0957199,"threshold_uncertainty_score":0.1903255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1157577099122224,"score_gpt":0.402875549658632,"score_spread":0.2871178397464095,"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."}}