{"id":"W2968619595","doi":"10.1109/tte.2019.2934345","title":"Technoeconomic Models for the Optimal Inclusion of Hydrogen Trains in Electricity Markets","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Electricity; Environmental economics; Hydrogen production; Electrification; Electricity market; Scheduling (production processes); Computer science; Environmental science; Hydrogen; Economics; Engineering; Operations management; Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0002341894,0.0001647433,0.0001938513,0.0002861087,0.0001240456,0.000008415748,0.0001940311,0.0001751162,0.00003620052],"category_scores_gemma":[0.000001238125,0.000151251,0.0001204733,0.0005924389,0.00002046103,0.0002261503,3.420558e-8,0.0002998229,0.000003565769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466042,"about_ca_system_score_gemma":0.00004627387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002114598,"about_ca_topic_score_gemma":0.00009552106,"domain_scores_codex":[0.9989208,0.00001739689,0.0004528627,0.0002186679,0.0001503922,0.0002398821],"domain_scores_gemma":[0.9994617,0.0001405935,0.00008256284,0.0002288912,0.00005696727,0.00002931265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001015194,0.00004099123,0.00001896837,0.00003824491,0.00002915371,5.989002e-8,0.0003249107,0.6611687,0.3044935,0.0004032556,0.000009659196,0.03337109],"study_design_scores_gemma":[0.0005038708,0.00008475201,0.001112329,0.00001035777,0.00002785371,5.878952e-7,0.00002294081,0.4469373,0.5503986,0.000709168,0.00008167129,0.0001105518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4826518,0.0001409115,0.5162458,0.00005094055,0.00006133988,0.0006769532,0.00004302144,0.00007330308,0.00005586691],"genre_scores_gemma":[0.998675,0.0004203213,0.0005652718,0.00002903492,0.00001210307,0.0001814126,0.00003611017,0.00003662282,0.00004413638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5160232,"threshold_uncertainty_score":0.6167837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006280403718986635,"score_gpt":0.1980667368046555,"score_spread":0.1917863330856688,"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."}}