{"id":"W4406904810","doi":"10.2139/ssrn.5114888","title":"Road Geometry's Effect on Vehicle Emissions/Energy Using Explainable Machine Learning","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Energy (signal processing); Geometry; Computer science; Artificial intelligence; Automotive engineering; Aerospace engineering; Engineering; Mathematics; Statistics","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":[0.0009722563,0.0004899318,0.0004523693,0.0003764308,0.0002211468,0.0006317619,0.0005567354,0.0008689206,0.004220687],"category_scores_gemma":[0.007283428,0.0002691097,0.0007589589,0.0004303177,0.0005005088,0.0007162822,0.000421595,0.0009403155,0.0002785863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000599551,"about_ca_system_score_gemma":0.0005175025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01964863,"about_ca_topic_score_gemma":0.01264123,"domain_scores_codex":[0.9997037,0.0001407916,0.00000921945,0.00006171289,0.00002963277,0.00005485859],"domain_scores_gemma":[0.992728,0.006225406,0.0003035364,0.0003969864,0.0002269964,0.0001191078],"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":[0.0003319307,0.00009408301,0.03500652,0.00003464856,0.000117992,0.0001167186,0.00004023264,0.9531769,0.001181489,0.002420247,0.0004086082,0.007070551],"study_design_scores_gemma":[0.00002457246,0.00008295335,0.03092837,0.000004984302,0.00006718869,0.00002044911,0.00003211471,0.9637618,0.0007859102,0.004070671,0.0002074104,0.00001359393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798147,0.0001495998,0.01745424,0.0004991224,0.00003293367,0.000008043038,0.0003426331,0.0001694292,0.001529294],"genre_scores_gemma":[0.9989362,0.00003582707,0.0005481909,0.00001086436,0.000005531587,0.000002499781,0.0001065853,0.00001320246,0.0003410173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01964863,"threshold_uncertainty_score":0.03906852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006864505649492857,"score_gpt":0.2349114403259721,"score_spread":0.2280469346764793,"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."}}