{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00543847,0.001153695,0.002081567,0.002866155,0.0009369391,0.005727349,0.005818977,0.00309536,0.004009682],"category_scores_gemma":[0.01687657,0.001062818,0.002004905,0.003799352,0.002783521,0.01337544,0.00416631,0.004231206,0.001481057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0021021,"about_ca_system_score_gemma":0.002288914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004316173,"about_ca_topic_score_gemma":0.004072784,"domain_scores_codex":[0.9942214,0.002065628,0.0004310935,0.001373188,0.001657522,0.0002512286],"domain_scores_gemma":[0.9930394,0.003753093,0.0006299083,0.001580004,0.0007410097,0.0002565273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006755866,0.00004252938,0.0005167047,0.0001412294,0.00006973198,0.0002288948,0.00025084,0.1143396,0.001061028,0.8266354,0.003637859,0.05300858],"study_design_scores_gemma":[0.0000132905,0.00002405486,0.00007104332,0.00003120766,0.00003401859,0.0001542809,0.00002555809,0.5239241,0.0007604288,0.4670867,0.007843077,0.00003219047],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000641029,0.0001535548,0.9977017,0.0002372371,0.00002423012,0.00002184306,0.0001497622,0.0002999052,0.0007707853],"genre_scores_gemma":[0.1198662,0.00146703,0.8723136,0.0006095095,0.000296639,0.0004201074,0.001102661,0.0002822595,0.003642075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005818977,"threshold_uncertainty_score":0.02876174,"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."}}