{"id":"W3146725752","doi":"10.31593/ijeat.798799","title":"A multivariate nonlinear regression model for the resistance power of a light rail vehicle","year":2021,"lang":"en","type":"article","venue":"International Journal of Energy Applications and Technologies","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Theoretical Astrophysics","keywords":"Energy consumption; Nonlinear system; Rolling resistance; Power (physics); Automotive engineering; Multivariate statistics; Process (computing); Energy (signal processing); Nonlinear regression; Light rail; Fuel efficiency; Regression analysis; Efficient energy use; Engineering; Computer science; Transport engineering; Structural engineering; Mathematics; Electrical engineering; Statistics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001445997,0.001149015,0.0007611263,0.0008126368,0.0003878716,0.00117067,0.001742425,0.001227734,0.003608668],"category_scores_gemma":[0.002157463,0.0004892969,0.001362192,0.00108286,0.0004531004,0.001172487,0.0005688063,0.001741334,0.001137544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170805,"about_ca_system_score_gemma":0.001064816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02791835,"about_ca_topic_score_gemma":0.01688259,"domain_scores_codex":[0.9991571,0.0002233776,0.00003915659,0.0002963932,0.000157479,0.0001265139],"domain_scores_gemma":[0.9992803,0.0003583344,0.0001225689,0.00003123927,0.0001890773,0.00001859966],"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":[0.00007401623,0.000111804,0.005992481,0.00008881465,0.00006870163,0.0001217252,0.00009518812,0.9704742,0.001354543,0.003114395,0.0008364632,0.01766769],"study_design_scores_gemma":[0.000003257754,0.00002988413,0.001671329,0.000006196823,0.00001261595,0.00001357259,0.00001726927,0.9972242,0.000162849,0.0005062066,0.0003409325,0.00001161021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3071569,0.0009519553,0.6799829,0.0007906461,0.0001847623,0.0002299569,0.001529015,0.001276867,0.007896925],"genre_scores_gemma":[0.9645249,0.0006821031,0.02116696,0.00004853858,0.00004274529,0.0002723917,0.001075692,0.00008541336,0.0121013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02791835,"threshold_uncertainty_score":0.05551165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01067322164390939,"score_gpt":0.2536912577689061,"score_spread":0.2430180361249967,"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."}}