{"id":"W1930672895","doi":"","title":"Urban logistics solutions and financing mechanisms : a scenario assessment analysis","year":2013,"lang":"en","type":"article","venue":"OpenstarTs (Univeristy of Trieste https://www.units.it/)","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Attractiveness; Business; Set (abstract data type); City logistics; Cost–benefit analysis; Field (mathematics); Transport engineering; Finance; Environmental economics; Risk analysis (engineering); Computer science; Economics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002144393,0.0008883689,0.0003116743,0.004058884,0.0006472005,0.003174004,0.0007980893,0.001539764,0.009137006],"category_scores_gemma":[0.003088762,0.0003734119,0.001027826,0.004008125,0.0006744527,0.002514303,0.001777047,0.0009322686,0.0005346269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004396653,"about_ca_system_score_gemma":0.002128559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01164018,"about_ca_topic_score_gemma":0.01103658,"domain_scores_codex":[0.9981298,0.001260657,0.00003954022,0.0000783825,0.0003014053,0.0001903305],"domain_scores_gemma":[0.998719,0.0008163605,0.0001189671,0.00006402323,0.0001830202,0.00009873752],"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":[0.0001470231,0.0003442847,0.00785418,0.0002370672,0.00009642333,0.0007933526,0.0003581858,0.657902,0.000706734,0.2928472,0.004140441,0.03457316],"study_design_scores_gemma":[0.00008559579,0.0003833453,0.007796035,0.0003052443,0.000111802,0.0003245153,0.003539915,0.8651019,0.00168848,0.08412527,0.03643291,0.0001050141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5435761,0.001435962,0.08327315,0.003844036,0.00006784871,0.00159834,0.005217449,0.0001953485,0.3607918],"genre_scores_gemma":[0.9623136,0.001700519,0.02138732,0.0001195902,0.00002161099,0.0006711841,0.00111596,0.00002433378,0.01264593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01164018,"threshold_uncertainty_score":0.03190005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.041026290356179,"score_gpt":0.2117379685244527,"score_spread":0.1707116781682737,"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."}}