{"id":"W2394807234","doi":"","title":"The UC3M team at the Knowledge Base Population task.","year":2009,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Spurious relationship; Computer science; Task (project management); Knowledge base; Entity linking; Baseline (sea); Population; Similarity (geometry); Information retrieval; Base (topology); Open source; Information extraction; Data mining; World Wide Web; Artificial intelligence; Machine learning; Software; Mathematics; Engineering; Programming language","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":[0.02116182,0.002405279,0.003113629,0.005593489,0.00229829,0.005756226,0.004233734,0.003532154,0.05545247],"category_scores_gemma":[0.06353375,0.001293818,0.002660548,0.004086459,0.001075697,0.00799132,0.005909604,0.005316102,0.02288388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003936207,"about_ca_system_score_gemma":0.005514972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01420178,"about_ca_topic_score_gemma":0.00953321,"domain_scores_codex":[0.986126,0.005460273,0.0009465963,0.003306376,0.003327301,0.0008334593],"domain_scores_gemma":[0.9537999,0.01807224,0.0006589056,0.01064431,0.01185563,0.004969008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001716254,0.001086736,0.003565541,0.0009194444,0.000344274,0.0007889157,0.0018323,0.01119244,0.0117984,0.01233956,0.5420276,0.4123885],"study_design_scores_gemma":[0.002266659,0.000802149,0.006939854,0.0003504506,0.0003921533,0.0008060607,0.001936578,0.269818,0.03861706,0.0287714,0.6489924,0.0003071945],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05388254,0.002194155,0.7475191,0.01324458,0.002959759,0.004960841,0.05080648,0.08564068,0.03879185],"genre_scores_gemma":[0.09187216,0.0004536038,0.7972837,0.0015142,0.0005364393,0.002301199,0.07094555,0.009303174,0.02579004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05545247,"threshold_uncertainty_score":0.185507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04235524182396751,"score_gpt":0.3666270019531516,"score_spread":0.3242717601291841,"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."}}