{"id":"W4407658241","doi":"10.1016/j.trb.2025.103172","title":"A contextual framework for learning routing experiences in last-mile delivery","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mile; Routing (electronic design automation); Last mile (transportation); Computer science; Transport engineering; Environmental science; Engineering; Geology; Computer network","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":[0.007832596,0.0001571875,0.0003505408,0.0003176216,0.0002262796,0.00005732554,0.0002343504,0.0002757303,0.0001628373],"category_scores_gemma":[0.005384279,0.0001526689,0.00009135714,0.001059801,0.000159284,0.0001270984,0.00001139568,0.0009091635,0.000006086639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007711325,"about_ca_system_score_gemma":0.00006064205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001916097,"about_ca_topic_score_gemma":0.00005072388,"domain_scores_codex":[0.9964189,0.001727832,0.0005531666,0.0003759237,0.0003288828,0.0005953179],"domain_scores_gemma":[0.9881942,0.01132757,0.00003787222,0.0001484489,0.0002067146,0.00008524002],"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":[0.0003970453,0.0001132787,0.06107425,0.0002975682,0.00007568108,0.00002759395,0.01806339,0.8035538,0.003425573,0.06712011,0.0004775659,0.04537414],"study_design_scores_gemma":[0.002585064,0.0004884715,0.08081881,0.0008187162,0.00003812241,0.000001325595,0.06175509,0.7989702,0.01514652,0.02434766,0.01414726,0.0008827222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3837169,0.0001417852,0.6149089,0.00009324869,0.0001562413,0.0003646803,0.00000411868,0.0002124871,0.0004016653],"genre_scores_gemma":[0.55766,0.00006673476,0.4415212,0.00003778697,0.00004712813,0.0005186262,0.00002147732,0.00001529766,0.000111713],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1739431,"threshold_uncertainty_score":0.6445874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2988142882395745,"score_gpt":0.4749499398335362,"score_spread":0.1761356515939617,"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."}}