{"id":"W1481901344","doi":"10.1007/978-3-540-79355-7_23","title":"An OCL-Based CSP Specification and Solving Tool","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Constraint satisfaction problem; Constraint programming; Computer science; Constraint satisfaction; Object Constraint Language; Constraint (computer-aided design); Constraint logic programming; Concurrent constraint logic programming; Usability; Programming language; Representation (politics); Theoretical computer science; Range (aeronautics); Local consistency; Mathematical optimization; Unified Modeling Language; Mathematics; Artificial intelligence; Human–computer interaction; Software; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002175228,0.0002746146,0.0002904138,0.000342936,0.0002151814,0.00008798758,0.0003471669,0.0001225285,0.00005003708],"category_scores_gemma":[0.00007395867,0.000299623,0.00005485351,0.0001203338,0.0003792398,0.0003614248,0.0001209277,0.0002616287,0.00004030224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001824181,"about_ca_system_score_gemma":0.0001666236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004028032,"about_ca_topic_score_gemma":0.00002305349,"domain_scores_codex":[0.9982315,0.00003612418,0.0005366427,0.0006420237,0.0003855925,0.0001681387],"domain_scores_gemma":[0.9984922,0.0005087596,0.000237132,0.0003129641,0.0003944069,0.00005455682],"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":[0.000008816558,0.00002994905,0.0002118924,0.00004783958,0.00004349482,0.00003597384,0.001431788,0.3554241,0.000001698192,0.4673037,0.0004659624,0.1749947],"study_design_scores_gemma":[0.000145073,0.0001096515,0.001434012,0.0002948738,0.00001064056,0.00006252193,0.0001105874,0.8855903,0.0000333322,0.1068493,0.004740711,0.000619003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005103156,0.001434159,0.9814444,0.0004492897,0.0004957857,0.0002720469,0.000009029125,0.0001237956,0.01572051],"genre_scores_gemma":[0.4079357,0.007733975,0.5749168,0.001182005,0.00034831,0.00005613735,0.0001441221,0.00006794643,0.007615013],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5301662,"threshold_uncertainty_score":0.9999456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0989140523447704,"score_gpt":0.3371005366080038,"score_spread":0.2381864842632334,"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."}}