{"id":"W4407344253","doi":"10.1080/17480930.2025.2461557","title":"Evaluating operational benefits of fleet electrification in open pit mines using discrete event modeling; a case study for implementation of trolley assist trucks","year":2025,"lang":"en","type":"article","venue":"International Journal of Mining Reclamation and Environment","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Truck; Electrification; Engineering; Discrete event simulation; Transport engineering; Event (particle physics); Open-pit mining; Computer science; Automotive engineering; Simulation; Mining engineering","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":[0.0008676848,0.00008192335,0.0001954131,0.0002453959,0.00003102168,0.00003655883,0.0001415397,0.00003338882,0.00001884007],"category_scores_gemma":[0.00003369854,0.00008402613,0.00004104899,0.00004291235,0.000009133124,0.0002099658,0.00003213932,0.00004478774,3.726245e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001957107,"about_ca_system_score_gemma":0.00004253872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000869861,"about_ca_topic_score_gemma":0.00006527227,"domain_scores_codex":[0.9987924,0.00003236402,0.0008618384,0.00009587088,0.0001531925,0.00006435058],"domain_scores_gemma":[0.9994529,0.00006680877,0.0003169776,0.00006334512,0.00008206444,0.00001784983],"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":[0.0001206889,0.00008980247,0.0144759,0.00003116516,0.0001681046,0.000002324931,0.002778125,0.9363539,0.007584702,0.0002431582,0.00001760516,0.03813448],"study_design_scores_gemma":[0.002118889,0.0002587052,0.005103483,0.0001687173,0.00005936002,0.00004957111,0.005158264,0.9809589,0.005778919,0.0002472913,0.00001298867,0.00008487864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9351095,0.000103046,0.0642062,0.00009187556,0.0001037431,0.0003463684,0.0000161745,0.000002929686,0.00002013888],"genre_scores_gemma":[0.9706879,0.00007268428,0.02914422,0.000008631753,0.00002871483,0.00003042268,0.00001268395,0.000007960119,0.00000675436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04460499,"threshold_uncertainty_score":0.3426486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07401405539889884,"score_gpt":0.3950374734620229,"score_spread":0.3210234180631241,"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."}}