{"id":"W4291713137","doi":"10.1145/1807167.1807201","title":"GRN model of probabilistic databases","year":2010,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Probabilistic logic; Computer science; Probabilistic relevance model; Graphical model; Tuple; Probabilistic database; Formalism (music); Divergence-from-randomness model; Representation (politics); Database; Dependency (UML); Statistical model; Database theory; Theoretical computer science; Data modeling; Data mining; Artificial intelligence; Probabilistic analysis of algorithms; Relational database; 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":[0.003122195,0.0006480897,0.0009099256,0.001580215,0.0007309254,0.003184039,0.004068659,0.001787495,0.004319977],"category_scores_gemma":[0.01132606,0.0005833068,0.001516502,0.002265593,0.002175333,0.007344151,0.002108924,0.002632942,0.001057377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002449156,"about_ca_system_score_gemma":0.001779761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007955237,"about_ca_topic_score_gemma":0.005933468,"domain_scores_codex":[0.9960373,0.001149986,0.0002873004,0.001181307,0.001115651,0.0002284901],"domain_scores_gemma":[0.9950286,0.002492241,0.000548396,0.001022066,0.0007301855,0.0001786377],"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":[0.00004467406,0.00002444064,0.0004571152,0.00007778874,0.00003079982,0.0002366255,0.0003148875,0.1982565,0.00125549,0.7803231,0.001312715,0.01766578],"study_design_scores_gemma":[0.00001142764,0.00001487117,0.00007934631,0.00001547163,0.00001674476,0.0001137219,0.00002633324,0.6470435,0.0005781776,0.3475543,0.004530312,0.00001581151],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004646337,0.0001347111,0.9906139,0.0004252744,0.00002732678,0.00004615867,0.0004015522,0.000483898,0.003220767],"genre_scores_gemma":[0.3780014,0.0006691061,0.6092494,0.0005605876,0.0001203176,0.0005235896,0.00153224,0.0002238079,0.00911965],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007955237,"threshold_uncertainty_score":0.01776993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05829657324164689,"score_gpt":0.2806134611353955,"score_spread":0.2223168878937486,"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."}}