{"id":"W2059762168","doi":"10.1016/j.tre.2006.03.002","title":"Dynamic electric power supply chains and transportation networks: An evolutionary variational inequality formulation","year":2006,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Variational inequality; Supply chain network; Projected dynamical system; Mathematical optimization; Equivalence (formal languages); Unification; Electric power; Supply chain; Exploit; Computer science; Power (physics); Mathematics; Supply chain management; Physics; Dynamical systems theory","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007241433,0.0003289949,0.0004220427,0.0002424945,0.0003180408,0.00006253071,0.0001197317,0.0002221145,0.0001466258],"category_scores_gemma":[0.00001546536,0.0003322811,0.00008686691,0.0008879168,0.00009683639,0.0005382216,7.802634e-7,0.0005007423,0.000002963759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007880716,"about_ca_system_score_gemma":0.00007647933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002754014,"about_ca_topic_score_gemma":0.00176314,"domain_scores_codex":[0.9971654,0.0001220424,0.001015775,0.0004864811,0.0006753064,0.0005349596],"domain_scores_gemma":[0.9988316,0.0001592594,0.000139629,0.0002186076,0.0004486601,0.0002022162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002464835,0.0005146745,0.1193747,0.008339118,0.0002768748,0.000094039,0.0009998478,0.4378417,0.001775719,0.3975647,0.004496666,0.02847543],"study_design_scores_gemma":[0.0005671729,0.0001659659,0.8569777,0.0002985864,0.0001206297,0.000002213911,0.00002725223,0.1323973,0.00001842619,0.005196238,0.003866436,0.0003620816],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.613863,0.0648234,0.314846,0.0008058646,0.0002777648,0.002854394,0.00173799,0.0005666401,0.0002248813],"genre_scores_gemma":[0.9487267,0.03958701,0.001841153,0.0001134209,0.00007098242,0.0001423456,0.009435178,0.00004958584,0.00003358253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.737603,"threshold_uncertainty_score":0.9999129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219186156125092,"score_gpt":0.2958476789332314,"score_spread":0.2736558173719805,"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."}}