{"id":"W1968343045","doi":"10.1109/icde.2010.5447925","title":"Generator-Recognizer Networks: A unified approach to probabilistic databases","year":2010,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Graphical model; Probabilistic logic; Computer science; Probabilistic database; Tuple; Generator (circuit theory); Range (aeronautics); Probabilistic relevance model; Statistical model; Data modeling; Artificial intelligence; Database; Data mining; Theoretical computer science; Probabilistic analysis of algorithms; Database theory; Database design; Power (physics); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003349253,0.0001569368,0.0001437924,0.00006428419,0.0001075827,0.0002320001,0.0008828132,0.00005838971,0.00003294736],"category_scores_gemma":[0.00009423343,0.0001260318,0.00003525624,0.0004355626,0.00003446556,0.000285231,0.0003097652,0.0002572305,0.000122707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008881021,"about_ca_system_score_gemma":0.00009420165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006876641,"about_ca_topic_score_gemma":0.00005545399,"domain_scores_codex":[0.9986789,0.00003964569,0.0001967222,0.0005586842,0.0001814891,0.000344594],"domain_scores_gemma":[0.9986355,0.00005946039,0.0000313779,0.0008922304,0.0001251141,0.000256271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005918929,0.0002347967,0.0001389624,0.00001385056,0.00001352789,0.000003957581,0.0001674003,0.01304575,0.002668586,0.9291241,0.01410176,0.04048145],"study_design_scores_gemma":[0.0001203287,0.00002921678,0.0001660968,0.000006626027,0.000004930919,0.00001691308,0.000006999007,0.9921247,0.0004817776,0.002710395,0.004046658,0.0002853248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00952912,0.00001467494,0.9726498,0.0003017684,0.0004804304,0.0001976966,0.000002481062,0.000329082,0.01649498],"genre_scores_gemma":[0.5729706,0.000001403877,0.4251503,0.001102909,0.0001502775,0.00004040382,0.000007777866,0.000008121561,0.000568237],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.979079,"threshold_uncertainty_score":0.5139427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04648384942293247,"score_gpt":0.2645754740849444,"score_spread":0.2180916246620119,"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."}}