{"id":"W4411745362","doi":"10.1007/978-3-031-95976-9_10","title":"Acquiring and Selecting Implied Constraints with an Application to the BinSeq and Partition Global Constraints","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Partition (number theory); Operations research; Mathematics; Combinatorics","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":[0.0005146207,0.0002912762,0.0002359722,0.0002491189,0.0004292168,0.0006563406,0.0005729161,0.0001358585,0.00000716858],"category_scores_gemma":[0.0000459659,0.0002296754,0.00002061973,0.0005496226,0.001072918,0.0004241795,0.0003716383,0.0002741573,0.000002877423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001604619,"about_ca_system_score_gemma":0.0003823637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000182235,"about_ca_topic_score_gemma":0.0004284806,"domain_scores_codex":[0.9979689,0.00003328223,0.0002808233,0.001027615,0.0003602269,0.0003291703],"domain_scores_gemma":[0.9988226,0.0001859125,0.0001547055,0.0005022836,0.0001764021,0.0001581406],"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":[0.000006316588,0.000005388016,0.0004968211,0.00001331629,0.000006672828,0.000003960846,0.0003365388,0.008664353,0.00005953221,0.03697757,0.00000274518,0.9534268],"study_design_scores_gemma":[0.0006863285,0.000342781,0.007231219,0.0004998927,0.00002760083,0.0005731989,0.000006244332,0.9474253,0.0004531855,0.0415594,0.0003373486,0.0008575222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007817231,0.00004303852,0.9950821,0.001464297,0.0002185066,0.0005938928,0.000008901214,0.0001149259,0.00169256],"genre_scores_gemma":[0.7766515,0.00001496182,0.2217234,0.001489545,0.00007601111,0.00001604243,0.000005161525,0.000007607975,0.00001579239],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9525692,"threshold_uncertainty_score":0.9365893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009250595105475018,"score_gpt":0.2445720605606305,"score_spread":0.2353214654551555,"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."}}