{"id":"W4390748836","doi":"10.1016/j.jii.2024.100561","title":"Electric vehicle scheduling: State of the art, critical challenges, and future research opportunities","year":2024,"lang":"en","type":"article","venue":"Journal of Industrial Information Integration","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Software deployment; Scheduling (production processes); Electric vehicle; Computer science; Engineering; Operations research; Transport engineering; Automotive engineering; Operations management; Power (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008801206,0.000079118,0.0001278793,0.0003575695,0.00005967649,0.0001301165,0.0001059132,0.0001328277,0.00002035924],"category_scores_gemma":[0.0002205098,0.00005020713,0.00005333314,0.0003502108,0.00004194209,0.001337921,0.00001273257,0.001030132,0.000002520691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008889056,"about_ca_system_score_gemma":0.0001627272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000157543,"about_ca_topic_score_gemma":0.000001356163,"domain_scores_codex":[0.9988314,0.0000715995,0.0005543143,0.00003297304,0.0003771143,0.0001326253],"domain_scores_gemma":[0.9992222,0.0001371694,0.00008508427,0.000068835,0.0004335732,0.00005310413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003881997,0.00000682537,0.000003150253,0.00006583409,0.00003369168,0.000001308201,0.001649302,0.0005455943,0.002256212,0.0175263,0.009072455,0.9688005],"study_design_scores_gemma":[0.002006554,0.001934553,0.0007881849,0.001389228,0.00009997476,0.000424091,0.01035508,0.4048641,0.06696613,0.01965526,0.4911168,0.0004000205],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9362023,0.02402182,0.004639598,0.01950486,0.0050247,0.0005040359,0.00003594948,0.0001116791,0.009955019],"genre_scores_gemma":[0.9929101,0.00608855,0.000110743,0.00002595926,0.0008316928,0.000002172981,0.00000341095,0.000007542581,0.00001984238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9684005,"threshold_uncertainty_score":0.4475468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09288907346285498,"score_gpt":0.30168161113765,"score_spread":0.2087925376747951,"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."}}