{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001624277,0.001528557,0.0009074892,0.001672121,0.0008151652,0.002756663,0.002940596,0.001087167,0.03006919],"category_scores_gemma":[0.005376606,0.001484468,0.001510375,0.002273172,0.00106885,0.003375289,0.002267154,0.002949681,0.01001368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001155278,"about_ca_system_score_gemma":0.003041459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005687916,"about_ca_topic_score_gemma":0.00773879,"domain_scores_codex":[0.9985631,0.0002377705,0.0001713497,0.0001725428,0.0007404597,0.0001148798],"domain_scores_gemma":[0.9971768,0.001634972,0.0001383918,0.0004671095,0.0005020772,0.00008061946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001977259,0.0002094169,0.0004539573,0.001350092,0.00006758949,0.000455525,0.0006250622,0.02813458,0.01882265,0.2148023,0.1316342,0.6032469],"study_design_scores_gemma":[0.000275214,0.00008097337,0.0002352244,0.0003533356,0.00009542351,0.0007948683,0.0001454462,0.3351402,0.05768372,0.1438109,0.4612563,0.0001284832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007444486,0.0000614684,0.9606016,0.0001584868,0.00005144328,0.0001105778,0.001001375,0.02811025,0.009160242],"genre_scores_gemma":[0.01798077,0.000287223,0.9554079,0.0004216642,0.00003689647,0.0004092719,0.003809079,0.008726723,0.01292055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03006919,"threshold_uncertainty_score":0.1005914,"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."}}