{"id":"W2907654789","doi":"10.1049/hve.2018.5087","title":"Utilising a Lagrangian approach to compute maximum fault current in hybrid AC–DC distribution grids with MMC interface","year":2019,"lang":"en","type":"article","venue":"High Voltage","topic":"HVDC Systems and Fault Protection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Interface (matter); Lagrangian; Current (fluid); Fault (geology); Distribution (mathematics); Electrical engineering; Computer science; Electronic engineering; Physics; Mechanics; Engineering; Mathematics; Mathematical analysis; Geology","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":[0.0001829336,0.0002152691,0.0002577541,0.0001063284,0.00004031435,0.00007737973,0.0001425042,0.00006573298,0.00001164139],"category_scores_gemma":[0.000005817185,0.0001910974,0.00004079286,0.000342634,0.00001354357,0.0001895814,0.00004258405,0.0002929255,0.000154619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002158372,"about_ca_system_score_gemma":0.00001013615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002385914,"about_ca_topic_score_gemma":0.00003314892,"domain_scores_codex":[0.9988684,0.00002676779,0.0002571298,0.0003090007,0.0002000668,0.0003386333],"domain_scores_gemma":[0.9995447,0.00001268791,0.00003633901,0.0002729417,0.00003720047,0.00009615349],"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":[0.0004428141,0.0007176843,0.01084442,0.003925427,0.0002176553,0.00005641968,0.005338253,0.7316879,0.06589549,0.002532034,0.008069732,0.1702722],"study_design_scores_gemma":[0.004103898,0.0005331357,0.04572936,0.001874229,0.00005082745,0.0001330309,0.0007599773,0.6974069,0.02264885,0.0001445965,0.2247365,0.001878756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5393946,0.0001487407,0.4586049,0.00001098748,0.0005898661,0.0004962547,0.00004736675,0.0001899577,0.0005172761],"genre_scores_gemma":[0.9991052,0.000007020429,0.00048286,0.00000716661,0.0001226515,0.00004938966,0.0001100095,0.00003633881,0.00007932845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4597106,"threshold_uncertainty_score":0.7792726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007306705536720639,"score_gpt":0.2083370015316964,"score_spread":0.2010302959949758,"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."}}