{"id":"W4378072080","doi":"10.1016/j.trd.2023.103792","title":"Pavement-effects on heavy-vehicle fuel consumption in cold climate using a statistical approach","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Fuel efficiency; Payload (computing); Greenhouse gas; Environmental science; Offset (computer science); Statistical analysis; Automotive engineering; Transport engineering; Computer science; Engineering; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005648722,0.0001611721,0.0001843802,0.0002072062,0.000139437,0.00001508948,0.0000634457,0.00009037405,0.0001378913],"category_scores_gemma":[9.674051e-7,0.0001611752,0.0000294519,0.0002413738,0.00009329447,0.0001116721,0.00000287723,0.0003255077,0.0000787709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007121885,"about_ca_system_score_gemma":0.000009304686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004111502,"about_ca_topic_score_gemma":0.00004845385,"domain_scores_codex":[0.9983153,0.00004341393,0.0003140626,0.000296684,0.0004723824,0.0005581159],"domain_scores_gemma":[0.9995626,0.00008536731,0.00001439297,0.000150729,0.000006085961,0.0001807971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002442764,0.0003645735,0.6612418,0.003234057,0.00006288264,0.000276145,0.002553632,0.3002003,0.02305434,0.002250508,0.0002824538,0.006235057],"study_design_scores_gemma":[0.001227276,0.0001342063,0.8639848,0.0001618314,0.00001682098,5.51915e-7,0.0001489637,0.1256096,0.003242824,0.00008043209,0.00513362,0.0002590455],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976854,0.0001331049,0.001190243,0.00004441433,0.00004623061,0.0005254163,0.0001342218,0.0001066258,0.0001342924],"genre_scores_gemma":[0.9939523,0.004960712,0.0004336433,0.00001562607,0.00003025621,0.0001724218,0.0003630982,0.00003224271,0.00003969363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2027431,"threshold_uncertainty_score":0.6572533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06917844112285326,"score_gpt":0.3084771670710765,"score_spread":0.2392987259482233,"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."}}