{"id":"W2188437695","doi":"","title":"LCC Approaches to Knowledge Base Population at TAC 2010.","year":2010,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Surprise; Knowledge base; Computer science; Task (project management); Context (archaeology); Population; Base (topology); Relation (database); Track (disk drive); Artificial intelligence; Data mining; Engineering; Mathematics; Geography; Systems 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":[0.01446242,0.001480559,0.001218491,0.007769493,0.00245723,0.007502407,0.005311349,0.002791491,0.01529898],"category_scores_gemma":[0.03788986,0.001604678,0.001784007,0.007209675,0.001668015,0.008601691,0.005361668,0.005284478,0.004955944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006834039,"about_ca_system_score_gemma":0.005507762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02556498,"about_ca_topic_score_gemma":0.03371978,"domain_scores_codex":[0.9881378,0.004615822,0.0006996418,0.002159077,0.003890715,0.0004971126],"domain_scores_gemma":[0.9779694,0.008826205,0.0006434451,0.00535725,0.006448758,0.0007549298],"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":[0.0001946192,0.0003193997,0.001517801,0.000555297,0.0001812636,0.0002410126,0.001482545,0.02657793,0.003733373,0.08450779,0.05421721,0.8264718],"study_design_scores_gemma":[0.0001370508,0.0001362865,0.00139754,0.0002632565,0.0001438093,0.0004293999,0.0007025736,0.5831633,0.01405707,0.1495707,0.2498585,0.0001405319],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003531474,0.001738246,0.9799076,0.001252887,0.0001726519,0.0005360795,0.001198043,0.006538463,0.005124579],"genre_scores_gemma":[0.03765827,0.0006818833,0.9490033,0.0004845159,0.0002033392,0.0007397172,0.004229087,0.0008541094,0.006145785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02556498,"threshold_uncertainty_score":0.07648551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04039580635658899,"score_gpt":0.25342152675725,"score_spread":0.213025720400661,"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."}}