{"id":"W4206739815","doi":"10.1109/access.2021.3138990","title":"Microgrid Digital Twins: Concepts, Applications, and Future Trends","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":200,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada)","funders":"Villum Fonden","keywords":"Microgrid; Computer science; Data science; Artificial intelligence; Control (management)","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":[0.00001522028,0.0001037507,0.00009436874,0.00004857239,0.00003577538,0.0005225218,0.0001522864,0.00008333139,0.0001029606],"category_scores_gemma":[0.00000135024,0.000110846,0.00002978702,0.0002878677,0.00003365785,0.001221527,0.00001788332,0.0001239885,0.00003692904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000196431,"about_ca_system_score_gemma":0.0000124069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.260859e-7,"about_ca_topic_score_gemma":0.000001274665,"domain_scores_codex":[0.999494,0.000003098543,0.0001558545,0.0001221423,0.00008898682,0.0001359489],"domain_scores_gemma":[0.9996993,0.00001760629,0.00001421475,0.0001647263,0.00003694407,0.00006719939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001804873,0.00002989198,0.001629266,0.0001140101,0.00005245848,0.00001017834,0.0001616006,0.0004814157,0.0004071152,0.000916568,0.02049636,0.9756993],"study_design_scores_gemma":[0.0002340712,0.000003575635,0.001261456,0.00001345401,0.000007889214,0.00004047633,0.0002288502,0.0001198761,0.01209955,0.0001676493,0.9856375,0.000185655],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.258858,0.01332039,0.01534626,0.001598402,0.00314166,0.0004462336,0.001091878,0.001918295,0.7042788],"genre_scores_gemma":[0.9977162,0.0005398272,0.0001414856,0.0001389833,0.0007642648,0.00007598887,0.0001353707,0.00002955895,0.0004583454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9755137,"threshold_uncertainty_score":0.503869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01532145901473052,"score_gpt":0.2731486851798444,"score_spread":0.2578272261651139,"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."}}