{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001912853,0.0001846117,0.0001873911,0.0001407555,0.000079234,0.0003867022,0.0008151207,0.00008441913,0.00003266202],"category_scores_gemma":[0.0001021865,0.0001549079,0.0000251053,0.0005196129,0.0000242278,0.0007905446,0.0003014635,0.0002305904,0.00007292267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005316433,"about_ca_system_score_gemma":0.000107639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002403474,"about_ca_topic_score_gemma":0.00009047898,"domain_scores_codex":[0.9984249,0.00003914047,0.0003223591,0.0005316218,0.0002459766,0.0004359494],"domain_scores_gemma":[0.9991781,0.0000765915,0.00005585173,0.0004495416,0.00009845354,0.0001414954],"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":[0.00000221062,0.00006879552,0.001869477,0.00005686944,0.000006439005,0.000002920368,0.003291767,0.1541997,0.002670921,0.7660532,0.0002439632,0.07153374],"study_design_scores_gemma":[0.00007298948,0.00001520109,0.0004407318,0.0000379869,9.188317e-7,0.000001920727,0.00005243929,0.8478735,0.0004107736,0.1508939,0.000004205618,0.0001954959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2458013,0.000008869441,0.7503996,0.0007472115,0.00004504013,0.0002505104,8.612857e-7,0.0001697923,0.002576808],"genre_scores_gemma":[0.7307281,6.834742e-7,0.2686939,0.0003789149,0.00001749264,0.00005603346,0.000001352738,0.000007120263,0.0001163788],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6936738,"threshold_uncertainty_score":0.6316962,"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."}}