{"id":"W2402585298","doi":"","title":"Off to a cold start: New York University's 2013 knowledge base population systems","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cold start (automotive); Base (topology); Population; Computer science; Knowledge base; Operations research; Mathematics; Engineering; Demography; Artificial intelligence; Sociology","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":[0.03063424,0.0006190534,0.001741441,0.002909384,0.003527444,0.004987887,0.003142634,0.001703429,0.007915934],"category_scores_gemma":[0.06147726,0.001178928,0.001019141,0.004628955,0.00141996,0.006518548,0.004373904,0.004182661,0.002737981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007971333,"about_ca_system_score_gemma":0.009949721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1881715,"about_ca_topic_score_gemma":0.2548998,"domain_scores_codex":[0.9861898,0.004909165,0.000914812,0.002539612,0.00471994,0.0007268084],"domain_scores_gemma":[0.9630652,0.01577803,0.0004119005,0.006982611,0.01159032,0.002171933],"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":[0.001867993,0.0007824057,0.01576462,0.0004490913,0.0004681413,0.0002928132,0.004678006,0.02376922,0.004759388,0.02244185,0.5093929,0.4153336],"study_design_scores_gemma":[0.002048699,0.0007225251,0.04421377,0.0002800228,0.0003943092,0.0002378221,0.004602942,0.2459523,0.02575592,0.03103743,0.644152,0.0006023701],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4476658,0.004291824,0.2912805,0.04888041,0.006257331,0.003773385,0.05513816,0.09629823,0.04641439],"genre_scores_gemma":[0.389488,0.0008311275,0.4305025,0.003553092,0.0008759419,0.003084299,0.1183823,0.0109233,0.04235942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1881715,"threshold_uncertainty_score":0.3741525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512373262703934,"score_gpt":0.2191008455124284,"score_spread":0.2039771128853891,"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."}}