{"id":"W231261888","doi":"10.1016/j.ijar.2010.11.006","title":"Join tree propagation utilizing both arc reversal and variable elimination","year":2010,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University; University of Regina","funders":"","keywords":"Join (topology); Computer science; Benchmark (surveying); Tree (set theory); Node (physics); Variable (mathematics); Arc (geometry); Algorithm; Theoretical computer science; Distributed computing; Data mining; 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":[0.003780661,0.0007261431,0.001377758,0.0026856,0.001344555,0.002554422,0.002885054,0.001383237,0.00843624],"category_scores_gemma":[0.0144384,0.0008036122,0.00148516,0.00320584,0.001025916,0.004657914,0.002621525,0.002804204,0.00180036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006530334,"about_ca_system_score_gemma":0.002821865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006032847,"about_ca_topic_score_gemma":0.01574094,"domain_scores_codex":[0.9978157,0.0004493616,0.0001640495,0.000356758,0.00101279,0.0002013499],"domain_scores_gemma":[0.9932137,0.003483499,0.0002599609,0.001633986,0.001200596,0.0002083185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005987535,0.0005560606,0.003222323,0.0003001849,0.0001909568,0.000298983,0.0004882209,0.09967081,0.01342383,0.1983694,0.009762063,0.6731184],"study_design_scores_gemma":[0.00009038542,0.00009584972,0.0005109073,0.00004709615,0.000171616,0.0002000713,0.0001022186,0.8336581,0.01665585,0.1382988,0.01011379,0.0000553309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006198772,0.0000806822,0.9899336,0.0001163485,0.00006570034,0.0001049947,0.0001423943,0.001309196,0.002048289],"genre_scores_gemma":[0.07844388,0.0001338935,0.9166771,0.00009414019,0.0000587578,0.00008000554,0.0004255723,0.0003885721,0.003698094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00843624,"threshold_uncertainty_score":0.02822202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01466313648497318,"score_gpt":0.2600672992083015,"score_spread":0.2454041627233283,"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."}}