{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005529664,0.0001451316,0.0001217572,0.0001218649,0.0001221026,0.0002945725,0.0003100733,0.00005381813,0.0002580813],"category_scores_gemma":[0.0006108319,0.0001055342,0.00001917181,0.000106105,0.0001590887,0.0002701724,0.00007314398,0.0001322103,0.0001811235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004394283,"about_ca_system_score_gemma":0.00008640424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002025668,"about_ca_topic_score_gemma":0.0003807426,"domain_scores_codex":[0.9985704,0.0001561178,0.0003442339,0.0003779536,0.0003669936,0.0001842699],"domain_scores_gemma":[0.9987041,0.0004741996,0.0001467934,0.0001788127,0.0004063273,0.00008974952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009604049,0.00001832226,0.0007082957,0.000002028167,0.000007195193,0.000003104457,0.00009193936,0.00002953976,0.0006001345,0.5096681,0.00006721785,0.4887945],"study_design_scores_gemma":[0.00008901334,0.000367064,0.02272251,0.0001064949,0.00000477189,0.00002393599,0.00008048103,0.4571303,0.002550877,0.5161907,0.0003706517,0.0003631715],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002484912,0.000006285558,0.9908699,0.002987257,0.0007180633,0.0001247083,0.000167897,0.00007267232,0.002568266],"genre_scores_gemma":[0.9768031,0.00009857378,0.02249409,0.0001480419,0.00007940064,0.00001281521,0.00002447525,0.000006608307,0.0003329101],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9743181,"threshold_uncertainty_score":0.4303558,"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."}}