{"id":"W4205158948","doi":"10.2139/ssrn.4012376","title":"Machine Learning for Data-Driven Last-Mile Delivery Optimization","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mile; Last mile (transportation); Computer science; Geography","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":[0.0004625754,0.0001092335,0.0001078584,0.00009089401,0.0004711431,0.00003183176,0.0002820875,0.0000265845,0.00005248748],"category_scores_gemma":[0.00005203333,0.0001203786,0.00003602965,0.00009135999,0.000009342673,0.0001913566,0.00008846638,0.001157195,0.000002018012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000585947,"about_ca_system_score_gemma":0.0001659385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006849373,"about_ca_topic_score_gemma":0.00004151848,"domain_scores_codex":[0.9986823,0.00003582257,0.0001721295,0.0001446462,0.0001376406,0.0008274344],"domain_scores_gemma":[0.9996632,0.00005024888,0.00006660439,0.0001499601,0.00003363357,0.00003638256],"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":[0.00002067869,0.00001145106,0.00002932589,0.000006277399,0.00005206978,8.566422e-7,0.0000302232,0.9902881,0.00002464878,0.001558435,0.000103149,0.007874834],"study_design_scores_gemma":[0.0004275649,0.0001390357,0.000002128397,0.000002319328,0.00002831575,0.00008008664,0.0001901801,0.9809613,0.00001966579,0.005938481,0.01207538,0.0001355433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006593631,0.001619147,0.996894,0.00005079807,0.0002639408,0.0001091455,0.00002577305,0.0001652981,0.0002125405],"genre_scores_gemma":[0.9508574,0.007180425,0.03962212,0.00003276374,0.0002695193,0.00003202075,0.00102107,0.00008860608,0.0008960427],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9572719,"threshold_uncertainty_score":0.5027502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01194308073570458,"score_gpt":0.2217091643494694,"score_spread":0.2097660836137648,"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."}}