{"id":"W2807207822","doi":"","title":"NAIST Participation in the TAC KBP 2016 Cold Start Slot Filling Task: Combining CNN-based and Bootstrapping-based Methods.","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":"Bootstrapping (finance); Computer science; Task (project management); Artificial intelligence; Pattern recognition (psychology); Mathematics; Econometrics; 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":[0.00607316,0.002238773,0.001728052,0.002467823,0.00241882,0.003187164,0.003393081,0.003436608,0.01429586],"category_scores_gemma":[0.02921308,0.000592289,0.0008485581,0.002046231,0.0006216327,0.006417051,0.003897526,0.003190385,0.01780756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001387917,"about_ca_system_score_gemma":0.003320425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02730923,"about_ca_topic_score_gemma":0.05963565,"domain_scores_codex":[0.9940246,0.002397047,0.0003777278,0.001560662,0.001044873,0.000595008],"domain_scores_gemma":[0.9886303,0.006084364,0.0003766654,0.001816926,0.002275806,0.0008160595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003534957,0.00117472,0.0123852,0.001456731,0.0003170789,0.0008445938,0.002034268,0.009753865,0.009678371,0.002441366,0.4397643,0.5166146],"study_design_scores_gemma":[0.0009263249,0.001027992,0.02585014,0.0008161963,0.0005681378,0.001296303,0.008069435,0.561802,0.02994096,0.02541788,0.3438559,0.0004288034],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5286577,0.01096292,0.139607,0.0125144,0.008085422,0.002677975,0.1077013,0.05636017,0.1334331],"genre_scores_gemma":[0.6700463,0.0009629155,0.1001951,0.00206308,0.00109804,0.001395045,0.1726445,0.002719096,0.04887582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02730923,"threshold_uncertainty_score":0.05430055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.026476614561299,"score_gpt":0.3072488217754716,"score_spread":0.2807722072141726,"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."}}