{"id":"W2790276044","doi":"10.1155/2018/1732091","title":"Minimizing the Impact of Large Freight Vehicles in the City: A Multicriteria Vision for Route Planning and Type of Vehicles","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Transport engineering; Flow network; Operations research; Total cost; City logistics; Fleet management; Typology; Transportation planning; Computer science; Business; Engineering; Automotive engineering; Mathematical optimization; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002700491,0.00008830528,0.0001956364,0.00007916165,0.00003442054,0.000007618878,0.00009644539,0.00004399806,0.000002827923],"category_scores_gemma":[0.00001890252,0.0000516771,0.00007842944,0.0001368476,0.00005988824,0.0001731743,7.50518e-7,0.0001102691,5.20512e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001443077,"about_ca_system_score_gemma":0.00001609356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000897331,"about_ca_topic_score_gemma":0.00004391651,"domain_scores_codex":[0.999262,0.00001461353,0.0004387148,0.00005321265,0.0001181011,0.0001133906],"domain_scores_gemma":[0.9994477,0.0001491185,0.0001670391,0.0000733973,0.0001411248,0.00002164162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00248583,0.0002928584,0.2748942,0.0005942112,0.0003370985,0.00004171404,0.06271226,0.1269752,0.5233861,0.0009595501,0.0001954639,0.007125468],"study_design_scores_gemma":[0.001375161,0.0005657293,0.9822597,0.0002462295,0.00007232477,0.000004717678,0.001013476,0.005681376,0.007912301,0.0006102753,0.0001807747,0.00007795269],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880936,0.0009038184,0.01065946,0.00002157428,0.0001177788,0.000125967,0.00005396109,0.000005367409,0.00001847614],"genre_scores_gemma":[0.995334,0.0001123162,0.004452273,0.000008811964,0.00006636063,0.000001355534,0.00001219378,0.00001165185,0.000001060183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7073655,"threshold_uncertainty_score":0.2107331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0336261230563702,"score_gpt":0.3123782876941228,"score_spread":0.2787521646377527,"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."}}