{"id":"W2971016812","doi":"10.5802/ojmo.2","title":"Revisiting a Cutting-Plane Method for Perfect Matchings","year":2020,"lang":"en","type":"preprint","venue":"Open Journal of Mathematical Optimization","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Toronto","funders":"","keywords":"Counterexample; Linear programming; Uniqueness; Mathematics; Cutting-plane method; Matching (statistics); Algorithm; Mathematical optimization; Point (geometry); Polynomial; Sequence (biology); Time complexity; Applied mathematics; Discrete mathematics; Integer programming; Mathematical analysis; Geometry","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":[0.002517632,0.002087311,0.002050778,0.0022307,0.001002874,0.002310359,0.003492586,0.002894417,0.01609202],"category_scores_gemma":[0.009480339,0.001564919,0.003728231,0.002996779,0.001601981,0.004439637,0.003641975,0.005876025,0.004814141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001526127,"about_ca_system_score_gemma":0.002018254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005043392,"about_ca_topic_score_gemma":0.004891167,"domain_scores_codex":[0.9975427,0.0005582224,0.0001551844,0.0005407238,0.001017236,0.0001859254],"domain_scores_gemma":[0.997494,0.001410915,0.0001213712,0.0004131616,0.0004677378,0.00009273825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002133056,0.0002303355,0.0006112551,0.0006358891,0.000180727,0.0002061365,0.0002806309,0.2646155,0.007697653,0.2519442,0.01364791,0.4597365],"study_design_scores_gemma":[0.00008357268,0.0001084529,0.0001213339,0.0001018196,0.00004195483,0.0001339371,0.00005514527,0.8078227,0.001917369,0.1627031,0.02687632,0.00003422996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00175758,0.0003475801,0.9936132,0.0002261596,0.0001361099,0.00006917398,0.00006772551,0.0003865882,0.003395897],"genre_scores_gemma":[0.0273638,0.0005664735,0.9657546,0.0002312788,0.0001509722,0.000163355,0.0002498851,0.0005491649,0.004970453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01609202,"threshold_uncertainty_score":0.05383319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06268050422370516,"score_gpt":0.3521685173705242,"score_spread":0.289488013146819,"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."}}