{"id":"W2414396530","doi":"","title":"Survey Propagation beyond Constraint Satisfaction Problems.","year":2016,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Local consistency; Computer science; Constraint satisfaction problem; Constraint (computer-aided design); Mathematical optimization; Artificial intelligence; Mathematics; Probabilistic logic","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":[0.006259127,0.001258251,0.001693382,0.002895851,0.001737113,0.002965482,0.003366246,0.002179374,0.01793392],"category_scores_gemma":[0.04903873,0.001059151,0.001591077,0.005457258,0.001753134,0.007717314,0.003815652,0.004027693,0.002792739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002041027,"about_ca_system_score_gemma":0.003003272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00938533,"about_ca_topic_score_gemma":0.0154383,"domain_scores_codex":[0.9945289,0.003220089,0.0002235772,0.0006832011,0.001008334,0.0003357871],"domain_scores_gemma":[0.9632564,0.02862293,0.001204278,0.003176858,0.00304631,0.0006932205],"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":[0.0002378242,0.000179933,0.002094622,0.0009867882,0.0003297902,0.0001924027,0.0002812723,0.07408769,0.0005796643,0.5600216,0.1036425,0.257366],"study_design_scores_gemma":[0.00005130061,0.00004239125,0.0003955026,0.0001745861,0.00007033769,0.00009361366,0.0001043462,0.3197207,0.0007584487,0.6441889,0.03437509,0.00002465619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006510377,0.003146481,0.962955,0.003992134,0.0004899127,0.0002588888,0.001743167,0.0008993509,0.02000465],"genre_scores_gemma":[0.2780663,0.004642453,0.6746991,0.003034739,0.001337972,0.0009990837,0.007408306,0.001232111,0.02857986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01793392,"threshold_uncertainty_score":0.059995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08947312638678116,"score_gpt":0.3303909803103093,"score_spread":0.2409178539235282,"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."}}