{"id":"W1794401605","doi":"10.1007/978-3-540-30201-8_40","title":"Improved Algorithms for the Global Cardinality Constraint","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cardinality (data modeling); Consistency (knowledge bases); Local consistency; Domain (mathematical analysis); Algorithm; Constraint (computer-aided design); Computer science; Range (aeronautics); Constraint satisfaction; Variable (mathematics); Constant (computer programming); Constraint satisfaction problem; Combinatorics; Discrete mathematics; Mathematics; Data mining; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007861173,0.0003960781,0.0003598836,0.0001627735,0.0004185597,0.0006070637,0.001952144,0.0002435361,0.00002702814],"category_scores_gemma":[0.0001084252,0.0003015938,0.0002148751,0.0004083615,0.001053697,0.0003423262,0.0005211864,0.0004206001,0.000007025137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007589216,"about_ca_system_score_gemma":0.001282786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006644734,"about_ca_topic_score_gemma":0.0001233326,"domain_scores_codex":[0.9973679,0.00002048536,0.0004340083,0.001114486,0.0005591478,0.0005039811],"domain_scores_gemma":[0.9978511,0.0004101629,0.0002455556,0.001019549,0.0003382625,0.0001354101],"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":[0.000003687753,0.000008243756,0.0000294962,0.00001424691,0.00001653906,0.000007036982,0.0001313115,0.05379692,0.000007668118,0.1055664,0.000006252068,0.8404122],"study_design_scores_gemma":[0.0004359635,0.00009446791,0.0002912295,0.00008760662,0.00001526497,0.00008891683,2.94262e-7,0.7850464,0.0001195109,0.2122936,0.001111279,0.0004154888],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000002355023,0.0002150278,0.9921998,0.00244515,0.002589319,0.0008564252,0.00003675873,0.0001494758,0.001505738],"genre_scores_gemma":[0.1151775,0.00004913607,0.8818908,0.002148536,0.0005459713,0.00003730119,0.000009111269,0.00002126119,0.0001204099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8399967,"threshold_uncertainty_score":0.9999436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02086016326025896,"score_gpt":0.2659771024728263,"score_spread":0.2451169392125673,"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."}}