{"id":"W2250060370","doi":"","title":"Approaches to Managing Freight in Metropolitan Areas","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Metropolitan area; Truck; Context (archaeology); Transport engineering; Externality; Greenhouse gas; Business; Traffic congestion; Sustainability; Environmental economics; Environmental planning; Engineering; Economics; Geography","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":[0.001763913,0.0009154197,0.000359294,0.001494217,0.002349449,0.006993801,0.00350689,0.00237999,0.004661515],"category_scores_gemma":[0.002236576,0.000417664,0.0005159874,0.002597611,0.002242523,0.004303646,0.005244672,0.001959651,0.0006009839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005714278,"about_ca_system_score_gemma":0.005356229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02516253,"about_ca_topic_score_gemma":0.04340874,"domain_scores_codex":[0.9987718,0.0004598227,0.00004705785,0.0002242811,0.0002224642,0.000274644],"domain_scores_gemma":[0.9992239,0.0001682697,0.0001292677,0.00008173335,0.0002468124,0.0001499711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003430522,0.0002030464,0.003304216,0.0005670976,0.0001419857,0.0006659052,0.006048464,0.1506921,0.001986769,0.6412532,0.01838201,0.1767209],"study_design_scores_gemma":[0.00004156562,0.0001538639,0.003508387,0.0006292862,0.0001201022,0.000369101,0.02774456,0.1386192,0.002143659,0.3899854,0.4366076,0.00007740584],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09049085,0.008479268,0.5577614,0.03948766,0.0004831277,0.001035498,0.0003167293,0.000626081,0.3013193],"genre_scores_gemma":[0.7561228,0.009555351,0.1913631,0.001154005,0.0002525669,0.0006416857,0.0002285602,0.0001137096,0.04056823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02516253,"threshold_uncertainty_score":0.0500322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04862568324192321,"score_gpt":0.1982041917373646,"score_spread":0.1495785084954414,"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."}}