{"id":"W2023759104","doi":"10.1007/pl00007190","title":"Constraint Satisfaction Methods for Applications in Engineering","year":2000,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Constraint satisfaction problem; Local consistency; Constraint (computer-aided design); Constraint satisfaction; Computer science; Mathematical optimization; Consistency (knowledge bases); Constraint logic programming; 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":[0.0001611935,0.0001446733,0.000141813,0.0001960057,0.00004348554,0.00008320739,0.0001668492,0.00004524288,0.00001873161],"category_scores_gemma":[0.00001291395,0.0001464417,0.00003799881,0.0004147134,0.00001266745,0.0002676497,0.00001516981,0.0001099739,0.000003753589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007954436,"about_ca_system_score_gemma":0.00003690972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000109046,"about_ca_topic_score_gemma":0.00000413217,"domain_scores_codex":[0.9992311,0.00001371292,0.0001898866,0.000273597,0.00007591425,0.0002157697],"domain_scores_gemma":[0.9993854,0.0002620077,0.00002932479,0.0002217151,0.00002883295,0.00007267854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002058836,0.000007114591,0.0003697828,0.00001915493,0.00001067241,6.856114e-7,0.0001236654,0.731083,0.0002010155,0.01051731,0.00001115779,0.2576543],"study_design_scores_gemma":[0.0003920109,0.0000318845,0.01437182,0.00004153257,0.000003522445,0.00002175309,0.000005926853,0.9789167,0.0002322584,0.00003194737,0.005754215,0.0001964023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002560288,0.00002473237,0.9961997,0.0001437294,0.0002015039,0.0004160455,0.000001484722,0.000371341,0.00008118187],"genre_scores_gemma":[0.1746587,0.000006578148,0.8251167,0.00003498055,0.00002985962,0.0001214039,0.000005007627,0.00001288215,0.00001393532],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2574579,"threshold_uncertainty_score":0.5971721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006510027227191836,"score_gpt":0.2428611679149319,"score_spread":0.23635114068774,"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."}}