{"id":"W7115688554","doi":"10.1016/j.trd.2025.105150","title":"A generative physics-informed reinforcement learning-based approach for construction of representative drive cycle","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Environment and Climate Change Canada; Directorate-General for Climate Action","keywords":"Reinforcement learning; Kinematics; Monte Carlo method; Reduction (mathematics); High fidelity; Fidelity; Key (lock); Transient (computer programming); Action (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001868564,0.0001676132,0.0002507945,0.0001476657,0.0001467828,0.000005057786,0.0001037116,0.0001002734,0.00004351739],"category_scores_gemma":[0.00002376412,0.0001684837,0.00007611469,0.0002409835,0.0004095472,0.0001230885,0.000004976601,0.0002925766,0.000001129539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009521646,"about_ca_system_score_gemma":0.00005040435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002499228,"about_ca_topic_score_gemma":0.000004686026,"domain_scores_codex":[0.9985986,0.00002010082,0.0004143585,0.00033504,0.0003378355,0.0002940689],"domain_scores_gemma":[0.9994301,0.0001584536,0.00008431774,0.0001908489,0.00007744895,0.00005884795],"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":[0.0001766087,0.0000726182,0.002425128,0.0002750432,0.00007701424,9.575015e-7,0.0005558787,0.9754722,0.004889418,0.01377358,0.00002509185,0.002256432],"study_design_scores_gemma":[0.004231496,0.0004892065,0.008398252,0.0001546106,0.0001355627,2.64757e-7,0.004075452,0.5354897,0.4359483,0.00481274,0.005804026,0.0004603267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04453408,0.0000461711,0.9537326,0.0001628693,0.00002108032,0.0008372237,0.00003810404,0.0001027144,0.0005251938],"genre_scores_gemma":[0.9553505,0.0002205895,0.04267576,0.00001039283,0.0000149449,0.0006920663,0.0006040505,0.00001647179,0.0004152014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9110568,"threshold_uncertainty_score":0.6870565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0309888387798907,"score_gpt":0.3059135259933259,"score_spread":0.2749246872134352,"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."}}