{"id":"W2782578453","doi":"10.1115/1.4039023","title":"Analysis and Efficiency Assessment of Direct Conversion of Wind Energy Into Heat Using Electromagnetic Induction and Thermal Energy Storage","year":2018,"lang":"en","type":"article","venue":"Journal of Energy Resources Technology","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Exergy; Energy storage; Wind power; Environmental science; Thermal energy storage; Process engineering; Thermal energy; Exergy efficiency; Grid energy storage; Electric power; Electric potential energy; Energy transformation; Power (physics); Nuclear engineering; Engineering; Thermodynamics; Electrical engineering; 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":[0.0001750898,0.0001481117,0.0005410662,0.001032418,0.00006263895,0.000009590547,0.0001599136,0.0001935636,0.00001473555],"category_scores_gemma":[0.000009392837,0.0001250607,0.0001068208,0.0006876838,0.0003040012,0.00006131994,0.00004811772,0.0001056752,2.111219e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004901513,"about_ca_system_score_gemma":0.00002299923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006125855,"about_ca_topic_score_gemma":0.00005139986,"domain_scores_codex":[0.9989727,0.00005415128,0.0004847975,0.0001374594,0.0001793258,0.0001715439],"domain_scores_gemma":[0.9993666,0.00003370887,0.0002487878,0.000161225,0.0001354862,0.00005412947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008881211,0.00008959736,0.007565442,0.00007367179,0.001556733,0.00001312219,0.0004298967,0.04735448,0.9308372,0.001297018,0.00001100227,0.01068309],"study_design_scores_gemma":[0.001315942,0.003259471,0.009115355,0.0002414287,0.002078729,0.0002857096,0.001424337,0.8203517,0.1583982,0.0004933344,0.00249561,0.0005401002],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9530994,0.003568459,0.04272873,0.00001573763,0.0001060301,0.000009471776,9.724532e-7,0.00002226635,0.0004489024],"genre_scores_gemma":[0.9988928,0.0004969651,0.0004828793,0.000005201316,0.00007888614,4.355063e-7,9.093846e-7,0.00001469778,0.00002722712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7729973,"threshold_uncertainty_score":0.5099826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003264350166657647,"score_gpt":0.2100507642145993,"score_spread":0.2067864140479417,"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."}}