{"id":"W4384406331","doi":"10.1002/net.22170","title":"Two‐stage stochastic one‐to‐many driver matching for ridesharing","year":2023,"lang":"en","type":"article","venue":"Networks","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Matching (statistics); Benchmark (surveying); Profitability index; Computer science; Set (abstract data type); Mathematical optimization; Stochastic modelling; Operations research; Mathematics; Economics; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001230467,0.00009145848,0.0001016777,0.0001070309,0.00008530304,0.00003112921,0.00008817425,0.00004630554,0.00005272303],"category_scores_gemma":[0.000009965358,0.0001105447,0.00004244112,0.0004331531,0.000007978468,0.00007184959,0.000009868879,0.0001270544,0.0000760763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003085107,"about_ca_system_score_gemma":0.00000641697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001119171,"about_ca_topic_score_gemma":0.0001316336,"domain_scores_codex":[0.9993435,0.000002799994,0.0001832776,0.0001341915,0.00007934046,0.000256882],"domain_scores_gemma":[0.9996613,0.00008038431,0.00001351556,0.0001481181,0.00003528275,0.0000614138],"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":[0.000003309284,0.000004739348,0.0000927244,0.00002156618,0.00002112003,7.728642e-7,0.0003289732,0.9843517,0.0004534567,0.01119709,0.00140113,0.002123438],"study_design_scores_gemma":[0.0004269579,0.00001265226,0.01374388,0.00005927476,0.00001989393,3.121158e-7,0.0001138792,0.9804811,0.00006215832,0.001033616,0.003835261,0.000211015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1574826,0.0000103143,0.839944,0.0000795423,0.000515853,0.0003288571,0.00002584687,0.0008695057,0.000743439],"genre_scores_gemma":[0.9964857,0.000003433976,0.002411563,0.0001607945,0.000200047,0.0001476972,0.0001165504,0.00003795274,0.0004362458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8390031,"threshold_uncertainty_score":0.4507883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399823508684135,"score_gpt":0.2633445478943576,"score_spread":0.2393463128075163,"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."}}