{"id":"W2148979021","doi":"10.1109/cjece.2015.2420995","title":"Energy Storage in Flywheels: An Overview","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Flywheel; Energy storage; Flywheel energy storage; Renewable energy; Upgrade; Computer science; Electric power system; Deferral; Automotive engineering; Reliability engineering; Engineering; Electrical engineering; Power (physics); Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00009312335,0.00008724017,0.0001562509,0.0002761231,0.00001033338,0.00004598552,0.00008700905,0.00004555927,0.000003778265],"category_scores_gemma":[0.00000812831,0.00008542785,0.00002376457,0.000213916,0.00000444931,0.0001606292,0.000003499001,0.0001396737,4.136288e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001045834,"about_ca_system_score_gemma":0.00009100499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002599769,"about_ca_topic_score_gemma":0.0007037484,"domain_scores_codex":[0.999491,0.00001114396,0.0001827485,0.00005600778,0.00005569429,0.0002033794],"domain_scores_gemma":[0.9993724,0.00001500899,0.00001632316,0.00004667445,0.00004041366,0.0005091393],"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":[0.000002585667,0.000004656332,0.000277698,0.00001007402,0.00001552324,0.0001283908,0.0001292506,0.9232705,0.00005581698,0.0008283833,0.000453951,0.07482317],"study_design_scores_gemma":[0.000315227,0.00009095495,0.00105008,0.00002687749,0.000004743844,0.0001195489,0.000002011193,0.9840642,0.00002665657,0.00007402764,0.01412164,0.0001040022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2063254,0.1146637,0.677412,0.0001152251,0.001187199,0.00007344715,0.000003078642,0.00007430477,0.0001455858],"genre_scores_gemma":[0.9968773,0.0004247843,0.002309122,0.00007020727,0.000297361,8.050651e-7,0.000001324095,0.00001544877,0.000003716408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7905518,"threshold_uncertainty_score":0.3483647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170741757476497,"score_gpt":0.1743909384735916,"score_spread":0.1626835208988266,"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."}}