{"id":"W2109015525","doi":"10.1613/jair.2155","title":"Consistency and Random Constraint Satisfaction Models","year":2007,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Backtracking; Constraint satisfaction problem; Local consistency; Constraint satisfaction; Constraint satisfaction dual problem; Constraint learning; Computer science; Constraint (computer-aided design); Consistency (knowledge bases); Robustness (evolution); Theoretical computer science; Mathematical optimization; Algorithm; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006257093,0.00009117843,0.0001906859,0.0006379364,0.0002611245,0.0002605031,0.0002569686,0.00008259949,0.00006825866],"category_scores_gemma":[0.0005316299,0.00007962769,0.00007362041,0.0006024985,0.0003864942,0.0008558912,0.00007971521,0.0005189006,0.00001542577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009803104,"about_ca_system_score_gemma":0.0002861557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005197343,"about_ca_topic_score_gemma":0.0001362888,"domain_scores_codex":[0.9977728,0.000194457,0.000749132,0.0001853636,0.000764938,0.0003333297],"domain_scores_gemma":[0.9973785,0.0008800818,0.0002095593,0.0001765017,0.001125359,0.0002299745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008940301,0.00002950611,0.0003658843,0.000006192679,0.00001522924,0.00004401762,0.0007300189,0.001594167,0.002594267,0.2252017,0.00004169505,0.7692879],"study_design_scores_gemma":[0.0004366988,0.0007989391,0.005127184,0.0001643253,0.00001775522,0.001574455,0.007080268,0.44331,0.03783061,0.5028616,0.0004097323,0.0003884789],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03659476,0.0001945316,0.9597119,0.001139514,0.0003680653,0.0001431042,6.158327e-7,0.00001825183,0.001829266],"genre_scores_gemma":[0.9608027,0.0003378758,0.03868393,0.00004070879,0.000110651,8.814554e-7,1.492704e-7,0.000004799398,0.00001827796],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.924208,"threshold_uncertainty_score":0.3247123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1417227492671828,"score_gpt":0.3914333672345828,"score_spread":0.2497106179674,"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."}}