{"id":"W1985395035","doi":"10.1016/j.applthermaleng.2013.10.051","title":"Exploring the potential synergy between micro-cogeneration and electric vehicle charging","year":2013,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"Edgewood Chemical Biological Center; Natural Resources Canada","keywords":"Electricity; Cogeneration; Profitability index; Revenue; Automotive engineering; Engineering; Environmental science; Waste management; Environmental economics; Electricity generation; Business; Electrical engineering; Economics; Finance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001395792,0.0002282336,0.0002371789,0.0001289111,0.000195604,0.0006073823,0.0004311165,0.0003459085,0.005474727],"category_scores_gemma":[0.000389953,0.0001199546,0.0001802599,0.0002433739,0.0002738295,0.0008921998,0.0004673158,0.0002327862,0.0003389098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548251,"about_ca_system_score_gemma":0.0002104815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003062296,"about_ca_topic_score_gemma":0.0006986374,"domain_scores_codex":[0.999935,0.00001487887,0.000001343007,0.000009219454,0.00002320704,0.00001625175],"domain_scores_gemma":[0.9998617,0.0000906108,0.000009090218,0.00001451989,0.00001518546,0.000008960046],"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":[0.001548389,0.0005706596,0.006001312,0.0005816186,0.0001625034,0.0009290902,0.0001844136,0.5013577,0.1846785,0.1031355,0.001884256,0.1989661],"study_design_scores_gemma":[0.0001358075,0.001005939,0.004628933,0.0000380011,0.00009166004,0.000418604,0.000494144,0.8355092,0.08037914,0.06518813,0.01205957,0.00005083689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7874714,0.001508734,0.1176346,0.0009610836,0.000247931,0.00006377221,0.00007355084,0.0002482005,0.09179062],"genre_scores_gemma":[0.995032,0.0001571244,0.002868958,0.00001841537,0.00000971909,0.000005025606,0.00000819115,0.000005703509,0.001894915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005474727,"threshold_uncertainty_score":0.01831478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006835628374194045,"score_gpt":0.1504206985352861,"score_spread":0.1435850701610921,"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."}}