{"id":"W1480349804","doi":"10.48550/arxiv.1212.2452","title":"Value Elimination: Bayesian Inference via Backtracking Search","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Backtracking; Inference; Value (mathematics); Bayesian probability; Bayesian inference; Computer science; Mathematics; Artificial intelligence; Econometrics; Statistics; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006676753,0.0004511366,0.0003936829,0.00033632,0.0002859862,0.000321691,0.002869976,0.0005003467,0.00008056849],"category_scores_gemma":[0.00004227083,0.0005356573,0.0002155261,0.0007376489,0.0001442516,0.0009057582,0.00255272,0.001237666,0.0003192845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002703008,"about_ca_system_score_gemma":0.0004004625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002482591,"about_ca_topic_score_gemma":0.00001566043,"domain_scores_codex":[0.9970956,0.0003022701,0.0003066506,0.00131338,0.0002410672,0.0007410552],"domain_scores_gemma":[0.9971562,0.0001986208,0.0002232745,0.001642855,0.000379573,0.0003994514],"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":[0.00001106025,0.0001377361,0.001378422,0.0001236751,0.00006547962,0.0001223432,0.0007698415,0.2158496,0.000101453,0.7690746,0.00006903391,0.01229672],"study_design_scores_gemma":[0.0001868127,0.00003378672,0.00165999,0.0001527387,0.00004630343,0.000009384163,0.00003211421,0.9105082,0.0003239161,0.08635313,0.000078307,0.0006153604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02667572,0.0001192215,0.9669757,0.0002576044,0.0007818915,0.0002251498,0.000007246438,0.0004009851,0.004556519],"genre_scores_gemma":[0.9863132,0.0001402015,0.01254465,0.0001419928,0.0001948898,0.000001542053,0.00001872441,0.00002593244,0.0006188879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9596375,"threshold_uncertainty_score":0.9997095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1193623849380879,"score_gpt":0.2327183567883872,"score_spread":0.1133559718502993,"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."}}