{"id":"W1732143764","doi":"10.48550/arxiv.1301.6748","title":"Contextual Weak Independence in Bayesian Networks","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Independence (probability theory); Conditional independence; Context (archaeology); Probabilistic logic; Consistency (knowledge bases); Bayesian network; Representation (politics); Inference; Class (philosophy); Computer science; Mathematics; Artificial intelligence; Statistics; Political science; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003891215,0.0004132031,0.0004465877,0.0003155054,0.0001055633,0.0002768551,0.002974523,0.0006920646,0.00005935425],"category_scores_gemma":[0.00003196391,0.0004823348,0.0001592028,0.0006489892,0.0001313302,0.0006097786,0.002279374,0.001656712,0.0001601097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002106796,"about_ca_system_score_gemma":0.0003138022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001107764,"about_ca_topic_score_gemma":0.0003064223,"domain_scores_codex":[0.9972646,0.0002224643,0.0003239514,0.001440264,0.0001360574,0.0006126449],"domain_scores_gemma":[0.9978016,0.0001162299,0.000235718,0.001394327,0.0001874184,0.0002647032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001177681,0.00007899408,0.003704615,0.00002572218,0.00002885436,0.0002404417,0.0001849989,0.5691791,0.000005981939,0.4225961,0.0004031683,0.003540345],"study_design_scores_gemma":[0.0003087834,0.00003475847,0.001121969,0.0001625402,0.00001202342,0.000005920224,0.00005297067,0.9189293,0.000009565102,0.07880235,0.00006126235,0.0004985956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04861968,0.0001278917,0.9451292,0.0001210263,0.0006059327,0.000277955,0.000003402926,0.0002531776,0.004861797],"genre_scores_gemma":[0.9961066,0.0002034397,0.002076504,0.0001847493,0.00007955444,0.000002941743,0.000007639858,0.00001908244,0.001319481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9474869,"threshold_uncertainty_score":0.9997628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06312915730289366,"score_gpt":0.1931289779692352,"score_spread":0.1299998206663416,"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."}}