{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003536192,0.001047022,0.00125717,0.001385207,0.0009386158,0.002196232,0.002606317,0.002299359,0.006910739],"category_scores_gemma":[0.01309731,0.001163366,0.001722088,0.003102928,0.001947783,0.003460619,0.002328791,0.002441832,0.00104776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001683592,"about_ca_system_score_gemma":0.002933845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01150887,"about_ca_topic_score_gemma":0.01722627,"domain_scores_codex":[0.9976752,0.001145753,0.0001352491,0.0003609559,0.0004825695,0.0002001625],"domain_scores_gemma":[0.9902745,0.008358434,0.0003429784,0.0005812189,0.0003308138,0.0001120528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003547978,0.0002223938,0.00231852,0.0005471465,0.0002959148,0.0002906716,0.0003246893,0.5050041,0.002034605,0.2236557,0.01172656,0.2532249],"study_design_scores_gemma":[0.00008065563,0.00002707178,0.0001861299,0.00002963857,0.00004457848,0.0000694111,0.00003926188,0.8258929,0.001261141,0.1699951,0.002359595,0.00001452464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01647676,0.0005877916,0.9755684,0.0008987068,0.00006614573,0.000099711,0.0001752321,0.001259832,0.004867474],"genre_scores_gemma":[0.1771095,0.0005854776,0.8164379,0.0003684589,0.0001243444,0.0002391958,0.0005513368,0.000215416,0.004368423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01150887,"threshold_uncertainty_score":0.02311873,"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."}}