{"id":"W2804943222","doi":"","title":"Reducing Freight Transport Pollution by using electric vehicles","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Truck; Payload (computing); Metropolitan area; Transport engineering; Environmental science; Tonnage; Business; Engineering; Automotive engineering; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001636597,0.0003233279,0.0003031672,0.0001911032,0.0002895455,0.0001071554,0.0006281125,0.0003410837,0.0001301441],"category_scores_gemma":[0.00008813719,0.0003134936,0.000151716,0.0003271041,0.00006574718,0.0001683866,0.0001652891,0.0005954277,0.0000212072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002285066,"about_ca_system_score_gemma":0.0001437282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003605082,"about_ca_topic_score_gemma":0.00004904265,"domain_scores_codex":[0.9977346,0.0006372085,0.0004602946,0.0004713251,0.0002851916,0.0004113548],"domain_scores_gemma":[0.9979241,0.0001755545,0.0001715309,0.001120607,0.0004362139,0.0001719867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001502838,0.0002654389,0.002590961,0.0006226415,0.0001829987,0.000006809759,0.004206874,0.007950822,0.7916642,0.003552285,0.006417899,0.182524],"study_design_scores_gemma":[0.0004555802,3.075705e-7,0.002209908,0.003605544,0.00006370913,0.00001633316,0.00002002124,0.3666321,0.6043109,0.001086386,0.020798,0.0008011947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7981871,0.004173043,0.1721607,0.001413083,0.0003049821,0.0002845273,0.0001287039,0.0005745901,0.02277329],"genre_scores_gemma":[0.9899189,0.001537618,0.006046931,0.000021054,0.00005251365,0.0000234476,0.0001784236,0.00007376922,0.002147291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3586813,"threshold_uncertainty_score":0.9999317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01182940663503775,"score_gpt":0.2113755053979487,"score_spread":0.199546098762911,"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."}}