{"id":"W4283525618","doi":"10.1177/03611981221100242","title":"Dynamic Surrogate Trip-Level Energy Model for Electric Bus Transit System Optimization","year":2022,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Energy consumption; Electrification; Computer science; Scheduling (production processes); Electric vehicle; Surrogate model; Simulation; Real-time computing; Automotive engineering; Mathematical optimization; Transport engineering; Electricity; Engineering; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002928075,0.0002921645,0.0004920266,0.001327339,0.001102619,0.000102019,0.001093423,0.000171746,0.0001316317],"category_scores_gemma":[0.00003271887,0.0002541823,0.0004773844,0.003067539,0.0001156379,0.0004578861,0.000005341928,0.002071782,0.000001369743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009527847,"about_ca_system_score_gemma":0.000595893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009310409,"about_ca_topic_score_gemma":0.004241962,"domain_scores_codex":[0.9937136,0.0006224564,0.001337616,0.0003659781,0.002918727,0.001041661],"domain_scores_gemma":[0.9966323,0.0004718917,0.0002714265,0.0003749697,0.001957569,0.0002918221],"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":[0.0009572721,0.00007360032,0.0005381257,0.0003222824,0.0001594226,0.00002798724,0.001046907,0.9790217,0.005663353,0.002082993,0.0032445,0.006861878],"study_design_scores_gemma":[0.002075715,0.0006765114,0.01130503,0.00008822711,0.00007968877,0.000003175535,0.001057857,0.9796658,0.00104409,0.001357795,0.00237705,0.0002691001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4961404,0.0007358175,0.4997139,0.0008727852,0.0006438276,0.001218705,0.0004484514,0.0001206021,0.0001054724],"genre_scores_gemma":[0.9917353,0.0008296508,0.006205086,0.00002897489,0.00008660521,0.000304267,0.00009300088,0.0001222543,0.0005948931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4955949,"threshold_uncertainty_score":0.9999911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04170605052919683,"score_gpt":0.2986256610510178,"score_spread":0.256919610521821,"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."}}