{"id":"W3088594052","doi":"10.1109/eucnc48522.2020.9200929","title":"Energy Credits Auction Mechanism for Enhancing the Grid’s Upward Flexibility Using Datacenters","year":2020,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Grid; Flexibility (engineering); Renewable energy; Distributed computing; Energy consumption; Cloud computing; Load balancing (electrical power); Efficient energy use; Environmental economics; Engineering; Operating system; Economics","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":[0.0003627145,0.0001201449,0.0001170131,0.00002724497,0.0003222273,0.0001799308,0.0007948275,0.00003074608,0.000004573305],"category_scores_gemma":[0.00005064323,0.00008227966,0.00008938197,0.000229018,0.00002069925,0.00005173395,0.0006678984,0.00006999179,0.000004373314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005057466,"about_ca_system_score_gemma":0.00002675579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001044435,"about_ca_topic_score_gemma":0.00002751909,"domain_scores_codex":[0.9987709,0.00006371927,0.0002218902,0.0004669992,0.0002222451,0.0002542309],"domain_scores_gemma":[0.9992248,0.00008423607,0.00008096453,0.0004667303,0.00006084138,0.0000824294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007269802,0.0001740238,0.0000299823,0.0002003287,0.0002349162,0.00001187173,0.005073097,0.1502566,0.01584825,0.6454521,0.0258437,0.1568024],"study_design_scores_gemma":[0.0001741919,0.00005905237,0.000009830576,0.00001067845,0.0000110479,0.000003031422,0.0001559937,0.975805,0.01070635,0.002814532,0.01013763,0.0001127016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01688979,0.00001900214,0.9737345,0.00749577,0.001168011,0.0001682304,0.00000181331,0.0002687965,0.0002540725],"genre_scores_gemma":[0.9536621,0.000001734767,0.04039304,0.00474752,0.0009385547,0.00001085367,0.000003126817,0.00001119432,0.0002318541],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9367723,"threshold_uncertainty_score":0.3355267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05427758665359864,"score_gpt":0.2631042117120155,"score_spread":0.2088266250584168,"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."}}