{"id":"W4319782946","doi":"10.1109/elticom57747.2022.10037958","title":"Optimal FCL Placement and Sizing Incorporate DOCR Settings to mitigate Escalated Fault Stresses in Distribution Network","year":2022,"lang":"en","type":"article","venue":"","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Malaya","keywords":"Sizing; Relay; Reliability engineering; Fault (geology); Fault current limiter; Grid; Computer science; Reliability (semiconductor); Circuit breaker; Software deployment; Engineering; Electric power system; Power (physics); Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000306964,0.0001554419,0.0001670881,0.00007728708,0.0001337361,0.00005641765,0.00008113413,0.00004240754,0.00004943152],"category_scores_gemma":[0.00001812231,0.0001755733,0.00001990294,0.0005402819,0.000009309862,0.0001250583,0.0001374847,0.0002161272,0.000009971952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003331574,"about_ca_system_score_gemma":0.00001046401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001173459,"about_ca_topic_score_gemma":0.0001148777,"domain_scores_codex":[0.9988834,0.00006122677,0.0003046255,0.000229638,0.0001848916,0.0003361694],"domain_scores_gemma":[0.9996826,0.00003641945,0.00004272481,0.0001267254,0.00001978266,0.00009171463],"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":[0.0000287347,0.00001015385,0.001552226,0.00003814191,0.00001949476,0.00001806205,0.0002851622,0.9891794,0.00305103,0.00002626754,0.003526201,0.002265145],"study_design_scores_gemma":[0.001090904,0.0002409558,0.006186201,0.0001546653,0.00001986904,0.00005716458,0.001679636,0.9175319,0.009347979,0.00004815613,0.06296226,0.0006803197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772667,0.0001576046,0.02072438,0.0001081962,0.0005262007,0.0003853864,0.00006327591,0.0003823468,0.0003859291],"genre_scores_gemma":[0.9989833,0.000005901058,0.0005720546,0.00003905167,0.00005774294,0.0001241977,0.00009789813,0.00002518064,0.00009465869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07164749,"threshold_uncertainty_score":0.715967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004505255891925507,"score_gpt":0.1978592816695949,"score_spread":0.1933540257776694,"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."}}