{"id":"W1972237262","doi":"10.1007/s10601-012-9136-9","title":"Using dual presolving reductions to reformulate cumulative constraints","year":2013,"lang":"en","type":"article","venue":"Constraints","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Solver; Computer science; Constraint programming; Dual (grammatical number); Benchmark (surveying); Integer programming; Scheduling (production processes); Variable (mathematics); Linear programming; Domain (mathematical analysis); Constraint (computer-aided design); State variable; Computational complexity theory; Mathematics; Algorithm; Stochastic programming","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002175965,0.0002414894,0.0002315413,0.0002987462,0.0003483244,0.0003588851,0.0003515741,0.0001547502,0.001861299],"category_scores_gemma":[0.0001974714,0.0002454091,0.00009062245,0.0005958164,0.0004493991,0.001246298,0.0002157202,0.0002812213,0.0005296689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001617511,"about_ca_system_score_gemma":0.0002391668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007039496,"about_ca_topic_score_gemma":0.000008289808,"domain_scores_codex":[0.998101,0.00008880089,0.0004578805,0.0005582189,0.0003042822,0.0004898424],"domain_scores_gemma":[0.9985577,0.00009109172,0.0001480484,0.0005062099,0.0003360148,0.0003609206],"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":[0.000008760704,0.0001397919,0.001453697,0.00002472761,0.0001716,0.00004627853,0.004782396,0.02372355,0.04969103,0.1707143,0.002355309,0.7468886],"study_design_scores_gemma":[0.00263976,0.0002108706,0.06903057,0.0004177237,0.00007443603,0.001950156,0.002308698,0.8954524,0.008027924,0.01416733,0.003220373,0.002499704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07143907,0.00001112498,0.9050222,0.001929319,0.0008621404,0.000788924,0.0000142911,0.0003829583,0.01954994],"genre_scores_gemma":[0.8084875,0.000002863489,0.1906541,0.0004722297,0.00007301996,0.00002658538,0.000004590402,0.00001385769,0.0002652054],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8717289,"threshold_uncertainty_score":0.9999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04453748127260258,"score_gpt":0.2950699864660795,"score_spread":0.2505325051934769,"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."}}