{"id":"W4413270905","doi":"10.1016/j.trc.2025.105278","title":"Generating practical last-mile delivery routes using a data-informed insertion heuristic","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Mile; Heuristic; Last mile (transportation); Computer science; Transport engineering; Engineering; Operations research; Geography; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097992,0.001182255,0.0009227207,0.000710754,0.0003948032,0.0009203526,0.002113493,0.001399013,0.003111908],"category_scores_gemma":[0.004080672,0.0007510232,0.0009076362,0.0007788245,0.000542009,0.001350951,0.0007807796,0.001532349,0.0006103919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001351842,"about_ca_system_score_gemma":0.001921576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0113504,"about_ca_topic_score_gemma":0.01901103,"domain_scores_codex":[0.9994819,0.0001602315,0.00002717872,0.0001614186,0.00007209838,0.00009729379],"domain_scores_gemma":[0.9981374,0.001184169,0.0001300542,0.0002088809,0.0002392832,0.0001002874],"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":[0.0001261841,0.0001608938,0.004026392,0.0000833261,0.00004187215,0.00009250155,0.00008637845,0.9583189,0.0008670466,0.002276222,0.002477819,0.03144249],"study_design_scores_gemma":[0.00002095441,0.000033486,0.0002012616,0.000005231921,0.000007806773,0.00001232336,0.00003762639,0.9978131,0.0003255375,0.001166123,0.0003715845,0.00000487811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5346775,0.0006345659,0.4502237,0.001087549,0.0001319766,0.0003369222,0.001652696,0.003721306,0.007533784],"genre_scores_gemma":[0.8654662,0.00009218093,0.1301342,0.0001740223,0.00001991377,0.0001907487,0.001832561,0.0002089503,0.001881268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0113504,"threshold_uncertainty_score":0.02256864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1870813155078189,"score_gpt":0.3881737265611778,"score_spread":0.201092411053359,"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."}}