{"id":"W2030290376","doi":"10.1016/j.tre.2009.02.002","title":"A stochastic programming winner determination model for truckload procurement under shipment uncertainty","year":2009,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Hedge; Mathematical optimization; Stochastic programming; Procurement; Combinatorial auction; Common value auction; Computer science; Integer programming; Operations research; Mathematics; Economics","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":[0.004356837,0.0002451924,0.0004376512,0.0002436426,0.0005963406,0.0001492512,0.0003160521,0.000107375,0.0001054128],"category_scores_gemma":[0.0003924465,0.0001993035,0.0001685963,0.00074838,0.0002355401,0.0002898856,0.000001948737,0.0002576188,0.00001793324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005497195,"about_ca_system_score_gemma":0.0001742671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001602156,"about_ca_topic_score_gemma":0.0005119622,"domain_scores_codex":[0.9957325,0.0001692515,0.001353028,0.0007523281,0.001535484,0.0004574074],"domain_scores_gemma":[0.9967914,0.0006903007,0.0003607993,0.0003933029,0.001513046,0.0002511705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002605233,0.0006140644,0.0002524605,0.001062788,0.000048404,0.000006345029,0.001749393,0.1402806,0.000138285,0.4624126,0.002116141,0.3910585],"study_design_scores_gemma":[0.002318789,0.0008008095,0.02059824,0.001634071,0.0004670456,0.000002328318,0.001057264,0.2286958,0.00008607245,0.6708531,0.07252389,0.0009626432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002936618,0.002488526,0.9851885,0.005423148,0.00005997042,0.003465818,0.0003032463,0.00006980587,0.00006436543],"genre_scores_gemma":[0.983987,0.00331817,0.008740939,0.0008924076,0.00006480692,0.001416197,0.0008323609,0.00002014472,0.000727987],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9810504,"threshold_uncertainty_score":0.812736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3585832754788283,"score_gpt":0.5011035883980925,"score_spread":0.1425203129192642,"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."}}