{"id":"W4399924090","doi":"10.2139/ssrn.4872999","title":"A Contextual Framework for Learning Routing Experiences in Last-Mile Delivery","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mile; Last mile (transportation); Routing (electronic design automation); Computer science; Business; Geography; Computer network","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003274334,0.0003681066,0.0004672569,0.0003396884,0.0001588016,0.0002751944,0.0004243084,0.0004787951,0.0000245927],"category_scores_gemma":[0.0007146281,0.0003917697,0.0002427548,0.0002799551,0.00003674175,0.00009165093,0.0002550046,0.01058272,0.00001247752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001651015,"about_ca_system_score_gemma":0.001337135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001935903,"about_ca_topic_score_gemma":0.000130388,"domain_scores_codex":[0.996251,0.0001914636,0.000662746,0.0003910512,0.0002607722,0.002242905],"domain_scores_gemma":[0.9990078,0.0004734316,0.0001669656,0.0001767468,0.00009252629,0.00008255338],"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":[0.00002660745,0.00001503407,0.0004409207,0.0001495003,0.0002322485,0.000008679284,0.01007144,0.9200662,0.00007648209,0.02608477,0.00002763814,0.0428005],"study_design_scores_gemma":[0.0003644907,0.0001196582,0.00001703388,0.0009593071,0.00005824328,0.0001112558,0.02913121,0.7484718,0.0001249285,0.2197574,0.0003324318,0.0005522618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3263254,0.009957797,0.6613333,0.00008085196,0.001387514,0.000274066,0.000003257996,0.0003376668,0.0003001324],"genre_scores_gemma":[0.9435489,0.002342551,0.05274295,0.00002039566,0.0008085428,0.0001368727,0.000009244999,0.0001430269,0.0002475811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6172234,"threshold_uncertainty_score":0.9998534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159098859996735,"score_gpt":0.2856285855236928,"score_spread":0.2697186995240193,"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."}}