{"id":"W2026627205","doi":"10.1016/j.cor.2013.03.001","title":"Achieving MILP feasibility quickly using general disjunctions","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Mathematical optimization; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001320862,0.0002188883,0.00028094,0.0005812303,0.001739263,0.0007082179,0.0006267253,0.0001220738,0.0009863683],"category_scores_gemma":[0.001375671,0.0002066523,0.0001066322,0.001491672,0.0003904804,0.0009726912,0.0005942255,0.0008246342,0.0003583212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006275459,"about_ca_system_score_gemma":0.0003996828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006124607,"about_ca_topic_score_gemma":0.0001423209,"domain_scores_codex":[0.9960462,0.0008455764,0.000533154,0.0006288073,0.001087583,0.0008586154],"domain_scores_gemma":[0.9959815,0.0007489446,0.00003208285,0.0009880969,0.00189377,0.0003556737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000439064,0.001899985,0.004048574,0.0002395882,0.0002297528,0.00002847315,0.002731009,0.8127254,0.02704691,0.07763359,0.04733443,0.02603841],"study_design_scores_gemma":[0.0004800476,0.00008194793,0.001324681,0.00004126929,0.000006884609,0.00001935219,0.0003017662,0.9920107,0.0003024006,0.004434485,0.0007477868,0.0002486399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.287523,0.0000513437,0.7084615,0.001284841,0.0002240938,0.001535366,0.00001080581,0.0001482208,0.000760862],"genre_scores_gemma":[0.1338317,0.00003628767,0.8589531,0.00009443316,0.0004418551,0.000275409,0.00004363572,0.00007677035,0.006246775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1792854,"threshold_uncertainty_score":0.9999269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3344502833390069,"score_gpt":0.5042806537434319,"score_spread":0.169830370404425,"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."}}