{"id":"W4360978592","doi":"10.1007/s10878-023-00998-8","title":"A detailed introduction to a minimum-cost perfect matching algorithm based on linear programming","year":2023,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Uniqueness; Theory of computation; Minimum weight; Linear programming; Matching (statistics); Algorithm; Mathematics; Computer science; Enhanced Data Rates for GSM Evolution; Cover (algebra); Combinatorics; Mathematical optimization; Artificial intelligence","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":[0.001456451,0.0001694855,0.0002747983,0.0006401404,0.0002047505,0.0002681797,0.0004575343,0.00008013816,0.00001238529],"category_scores_gemma":[0.0004971051,0.0001509107,0.0001312746,0.001521403,0.00001179382,0.0004389214,0.00007253548,0.0003822388,0.00003376015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001119708,"about_ca_system_score_gemma":0.0001261826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004134058,"about_ca_topic_score_gemma":1.202838e-7,"domain_scores_codex":[0.9980714,0.0002364108,0.0004822394,0.0002680487,0.000667951,0.0002739929],"domain_scores_gemma":[0.9986088,0.0001638651,0.0003848055,0.0002693908,0.000401655,0.0001714725],"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":[0.00005465748,0.0001106435,0.00003377982,0.000009148313,0.00001400612,0.00002442033,0.0003786772,0.9354677,0.00006311261,0.0004482551,0.0007832086,0.06261235],"study_design_scores_gemma":[0.001321434,0.0009565158,0.00006868987,0.00006465636,0.00001460904,0.00002200757,0.00002796449,0.9924181,0.0002548316,0.0003225889,0.004367645,0.0001609816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003610267,0.000006327661,0.9841169,0.004864243,0.006916231,0.0002669513,7.323537e-7,0.0001784406,0.00003986568],"genre_scores_gemma":[0.07282251,0.000008034801,0.9217383,0.0001906118,0.005077878,0.00001855076,0.00001456269,0.00003670791,0.00009284503],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06921224,"threshold_uncertainty_score":0.6153961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008549360243585968,"score_gpt":0.2621156453445673,"score_spread":0.2535662851009813,"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."}}