{"id":"W3142125358","doi":"10.1109/mper.2002.4312416","title":"Studies for Identification of Inrush Based on Improved Correlation Algorithm","year":2002,"lang":"en","type":"article","venue":"IEEE Power Engineering Review","topic":"Geoscience and Mining Technology","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inrush current; Waveform; Algorithm; Correlation coefficient; Fault (geology); Transformer; Sensitivity (control systems); Correlation; Identification (biology); Computer science; Scheme (mathematics); Control theory (sociology); Mathematics; Engineering; Electronic engineering; Artificial intelligence; Voltage; Electrical engineering; Machine learning; Geology; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003064804,0.000745712,0.001042909,0.002015245,0.0003982799,0.001435316,0.001037723,0.001046266,0.001821282],"category_scores_gemma":[0.01338286,0.0004186173,0.0006168608,0.001933431,0.0006668975,0.003392713,0.0006207293,0.001044892,0.0007552635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006422661,"about_ca_system_score_gemma":0.0009620035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002085851,"about_ca_topic_score_gemma":0.000792703,"domain_scores_codex":[0.997136,0.0007826724,0.0001983684,0.0004770107,0.001234731,0.0001712076],"domain_scores_gemma":[0.9912213,0.003395014,0.0005830201,0.0008997009,0.003793265,0.0001076546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009827164,0.0001951959,0.01289237,0.0005522497,0.0002763599,0.0005730235,0.0004203569,0.1614835,0.05872351,0.05844786,0.004494313,0.7009585],"study_design_scores_gemma":[0.00003131223,0.0001643276,0.002679643,0.00002978189,0.0000604079,0.00047623,0.00004010899,0.9645187,0.02440267,0.002785664,0.004764365,0.00004676934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02451235,0.001169155,0.9711662,0.0001276398,0.00007874573,0.00006468485,0.00002261385,0.0004966756,0.002362007],"genre_scores_gemma":[0.4190634,0.001962211,0.573723,0.0001573524,0.0002217628,0.0001393347,0.0002373034,0.000210742,0.004284924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003064804,"threshold_uncertainty_score":0.01620841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102765877287162,"score_gpt":0.2531754740678918,"score_spread":0.2321478152950202,"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."}}