{"id":"W3123543355","doi":"10.1287/msom.2020.0952","title":"Dynamic Type Matching","year":2021,"lang":"en","type":"preprint","venue":"Manufacturing & Service Operations Management","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Toronto","funders":"","keywords":"Matching (statistics); Hierarchy; Supply and demand; Disjoint sets; Optimal matching; Type (biology); Mathematical optimization; Computer science; Property (philosophy); Mathematics; Microeconomics; Economics; Statistics; Discrete 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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004857674,0.0008279108,0.0005894647,0.0008244104,0.0006888174,0.003923192,0.001337047,0.0002438527,0.002543933],"category_scores_gemma":[0.00001120137,0.0008954462,0.0002551241,0.0005557922,0.00003337066,0.001200527,0.006093349,0.0007418276,0.001669607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003549646,"about_ca_system_score_gemma":0.00004627079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002553806,"about_ca_topic_score_gemma":0.003910226,"domain_scores_codex":[0.9962905,0.00004008614,0.0007940731,0.001378232,0.0007699181,0.0007271655],"domain_scores_gemma":[0.9976544,0.00001599418,0.0002618748,0.001727175,0.0002968188,0.00004374215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006308078,0.0008706151,0.0001583835,0.01311901,0.001649242,0.0005501065,0.0009958395,0.9312266,0.0001539742,0.02272417,0.01170411,0.01678489],"study_design_scores_gemma":[0.002826167,0.00003555353,0.01814819,0.003367756,0.002699843,0.00001268514,0.01747808,0.3027426,0.0005605519,0.013314,0.6327847,0.006029944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8218688,0.0006040802,0.007853076,0.01199431,0.01028261,0.003563391,0.00001735437,0.001573334,0.1422431],"genre_scores_gemma":[0.9562802,0.0002641224,0.007418222,0.02272819,0.001137013,0.0004808539,0.002761637,0.0002347558,0.008694964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.628484,"threshold_uncertainty_score":0.9993496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829116646945745,"score_gpt":0.2367273536270173,"score_spread":0.2184361871575599,"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."}}