{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008275882,0.0009504338,0.001332948,0.003233711,0.002008453,0.004386662,0.00214833,0.002309705,0.003187239],"category_scores_gemma":[0.02673832,0.001509787,0.001607483,0.004272683,0.00608043,0.008377375,0.003681964,0.006332371,0.0006338614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003029429,"about_ca_system_score_gemma":0.001936204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006784307,"about_ca_topic_score_gemma":0.006179566,"domain_scores_codex":[0.9903389,0.004455491,0.0006254282,0.001778434,0.002376453,0.0004252784],"domain_scores_gemma":[0.9828184,0.01313654,0.001058318,0.001402055,0.0012153,0.0003693106],"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":[0.00001703685,0.000009937268,0.0003267938,0.00009415018,0.0000279462,0.00006924855,0.0001570325,0.01309776,0.0001667179,0.9753792,0.0007373604,0.009916948],"study_design_scores_gemma":[0.00000747279,0.000004236251,0.0001420125,0.00002718677,0.00001207341,0.00002984507,0.000016532,0.02885718,0.00009362103,0.9681857,0.002614588,0.000009428411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0108803,0.002195068,0.9701995,0.002117984,0.00009378248,0.00008209762,0.0004550473,0.0001874172,0.01378881],"genre_scores_gemma":[0.5736306,0.006399659,0.4077817,0.001298438,0.001120126,0.0007978457,0.001429047,0.0001550753,0.007387636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008275882,"threshold_uncertainty_score":0.04376757,"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."}}