{"id":"W4283813030","doi":"10.1609/aaai.v36i4.20296","title":"The SoftCumulative Constraint with Quadratic Penalty","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Penalty method; Mathematical optimization; Constraint (computer-aided design); Computer science; Quadratic equation; Quadratic growth; Scheduling (production processes); Decomposition; Function (biology); Algorithm; Mathematics","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":[0.002387753,0.001108709,0.0009562615,0.001227356,0.001538844,0.002625437,0.002324118,0.001537747,0.01033695],"category_scores_gemma":[0.01208275,0.0005358094,0.00106945,0.001744128,0.002292414,0.003532419,0.002945345,0.003114919,0.001404083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347949,"about_ca_system_score_gemma":0.00274774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004961327,"about_ca_topic_score_gemma":0.005708026,"domain_scores_codex":[0.997801,0.0004235696,0.0001078859,0.0004407936,0.0009537927,0.0002729026],"domain_scores_gemma":[0.9950368,0.002292634,0.0004156219,0.001057124,0.0008935597,0.0003042864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002938298,0.0001522784,0.001417017,0.0002606547,0.00006401682,0.0002647701,0.0001850382,0.2188869,0.006204836,0.5122241,0.0155162,0.2445304],"study_design_scores_gemma":[0.00003833881,0.0000488261,0.0002419937,0.00005186647,0.00001822488,0.0001340629,0.00004128859,0.8301293,0.005641507,0.1497671,0.01384954,0.00003801449],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01425887,0.0001568975,0.9735228,0.0005090603,0.0001735522,0.00008365999,0.0000873043,0.0006359296,0.01057187],"genre_scores_gemma":[0.267236,0.0002541319,0.7153608,0.0006624316,0.0001561933,0.0001895727,0.000364688,0.0007001965,0.01507603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01033695,"threshold_uncertainty_score":0.03458047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05134186170215016,"score_gpt":0.2721420053701765,"score_spread":0.2208001436680264,"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."}}