{"id":"W2949860269","doi":"10.48550/arxiv.1201.5426","title":"Constraint Propagation as Information Maximization","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Western University","funders":"","keywords":"Constraint satisfaction problem; Constraint satisfaction; Constraint satisfaction dual problem; Correctness; Constraint (computer-aided design); Local consistency; Mathematical optimization; Constraint logic programming; Maximization; Mathematics; Complexity of constraint satisfaction; Computation; Computer science; Algorithm; Constraint programming; Artificial intelligence","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":[0.005306476,0.001334782,0.001302095,0.002240533,0.001334798,0.003765276,0.002912458,0.001943035,0.006696504],"category_scores_gemma":[0.01976429,0.0008909663,0.001387688,0.003965699,0.004257833,0.005877524,0.004239256,0.003675598,0.001144504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003248881,"about_ca_system_score_gemma":0.00232603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003326488,"about_ca_topic_score_gemma":0.00256508,"domain_scores_codex":[0.9952211,0.001777541,0.0002439825,0.0007295848,0.001769166,0.000258561],"domain_scores_gemma":[0.988247,0.009077225,0.000517474,0.0009846236,0.001015573,0.0001579975],"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":[0.00003630845,0.0000225755,0.0001283141,0.0001702442,0.00003922309,0.00007699874,0.0001663296,0.09045121,0.0008032394,0.8595182,0.003379456,0.04520803],"study_design_scores_gemma":[0.00002255357,0.00001612089,0.00005813058,0.00004732892,0.00001650827,0.00004749146,0.00002832866,0.2906204,0.001272282,0.6982579,0.009594144,0.00001880798],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002754046,0.0006288222,0.9824567,0.001255254,0.00008412793,0.00007894561,0.0001047806,0.0001556361,0.01248167],"genre_scores_gemma":[0.2487486,0.003265574,0.7261565,0.001198429,0.0006592415,0.0008319864,0.000564499,0.0003567452,0.01821845],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006696504,"threshold_uncertainty_score":0.02806371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04087656527788575,"score_gpt":0.1734654184264373,"score_spread":0.1325888531485515,"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."}}