{"id":"W2156890722","doi":"10.1613/jair.2648","title":"Solving #SAT and Bayesian Inference with Backtracking Search","year":2009,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Backtracking; Memoization; Speedup; Look-ahead; Simple (philosophy); Probabilistic logic; Range (aeronautics); Inference; Exploit","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004457885,0.0001700367,0.0002925908,0.0006158378,0.000388771,0.000943996,0.001246283,0.0001035995,0.00002471127],"category_scores_gemma":[0.0004321789,0.0001293936,0.00006165569,0.001151289,0.0002792034,0.001136506,0.0001754216,0.001302264,0.0000309876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008391716,"about_ca_system_score_gemma":0.0005501556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003427639,"about_ca_topic_score_gemma":0.00002165733,"domain_scores_codex":[0.996563,0.0003307496,0.0006442337,0.0003640027,0.001378913,0.000719093],"domain_scores_gemma":[0.9969021,0.0006446866,0.00015556,0.0003894972,0.00151924,0.0003889283],"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":[0.00009979556,0.00015408,0.0003965413,0.00001933788,0.00001932894,0.0002158727,0.002621454,0.002165844,0.01082368,0.144714,0.00004622946,0.8387238],"study_design_scores_gemma":[0.0001188197,0.004426005,0.001615204,0.0008566815,0.00001326846,0.0006731815,0.002193754,0.5842074,0.07678433,0.3284352,0.0001285634,0.0005476694],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08920921,0.0003449867,0.90649,0.003151468,0.00008279351,0.0001013598,3.642329e-7,0.00002592558,0.000593926],"genre_scores_gemma":[0.9436479,0.0002557142,0.05578364,0.00008849671,0.0001854594,0.000001007481,1.773627e-7,0.000008304542,0.00002932339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8544387,"threshold_uncertainty_score":0.9102976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1682457707374162,"score_gpt":0.416986420039242,"score_spread":0.2487406493018257,"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."}}