{"id":"W3098407604","doi":"","title":"Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference","year":2013,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada); University of Waterloo","funders":"","keywords":"Backtracking; Computer science; Correctness; Bayesian network; Inference; Probabilistic logic; Algorithm; Theoretical computer science; Exploit; Approximate inference; Bayesian probability; Artificial intelligence","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":[0.008012343,0.002161035,0.001888062,0.002954659,0.001559021,0.003968715,0.005687293,0.002302185,0.007440864],"category_scores_gemma":[0.04619827,0.001925557,0.002685025,0.005404964,0.003168706,0.01344843,0.004178042,0.005450187,0.001550507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00373024,"about_ca_system_score_gemma":0.00348423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007453831,"about_ca_topic_score_gemma":0.01318036,"domain_scores_codex":[0.9894427,0.005595954,0.0005053238,0.001299652,0.002666695,0.0004897024],"domain_scores_gemma":[0.9709593,0.02185513,0.001274592,0.004314888,0.001361264,0.0002347608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002654001,0.0001503494,0.001249104,0.0004312831,0.0001762337,0.0002085684,0.0004138722,0.2965017,0.004112095,0.4677497,0.003699896,0.2250418],"study_design_scores_gemma":[0.00003730322,0.00002400861,0.00007968469,0.00004099126,0.00004492565,0.00005671075,0.00003063666,0.6416802,0.002532297,0.3533592,0.002087837,0.00002621225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003074428,0.0001375622,0.9944211,0.0002434175,0.00002340471,0.00005870828,0.00009664969,0.0006512957,0.001293546],"genre_scores_gemma":[0.1130089,0.0003684595,0.8837827,0.0002555354,0.00006482704,0.000262976,0.0004531009,0.0003751238,0.001428368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008012343,"threshold_uncertainty_score":0.04237384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.117026560476475,"score_gpt":0.2504234748558055,"score_spread":0.1333969143793305,"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."}}