{"id":"W4309327240","doi":"10.18698/1812-3368-2022-5-16-30","title":"Tangible Power Loss Dwindling by Canadian Yukon Cougar Optimization Algorithm","year":2022,"lang":"en","type":"article","venue":"Herald of the Bauman Moscow State Technical University Series Natural Sciences","topic":"Islanding Detection in Power Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Power (physics); Population; Geography; Value (mathematics); Power loss; Demography; Ecology; Biology; Mathematics; Statistics; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000425566,0.0008608552,0.0004734667,0.0007333935,0.0008116014,0.001251257,0.001193117,0.0006567236,0.004734713],"category_scores_gemma":[0.001128051,0.0002131099,0.0004020413,0.0006939856,0.0004253805,0.0004218603,0.0007391917,0.0005234929,0.0005054917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001742914,"about_ca_system_score_gemma":0.002941177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1299662,"about_ca_topic_score_gemma":0.1171237,"domain_scores_codex":[0.99971,0.00003784265,0.00001213808,0.00006098935,0.00009823878,0.00008077018],"domain_scores_gemma":[0.9997584,0.00004845214,0.00002240029,0.00001857985,0.0001350543,0.00001715706],"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":[0.00009976289,0.00003372318,0.002703453,0.00005999853,0.00003107202,0.00009523082,0.00008082089,0.8712486,0.001997601,0.006783721,0.004077268,0.1127887],"study_design_scores_gemma":[0.000009527267,0.00002748084,0.0006040977,0.000008772436,0.00001190364,0.00002927577,0.00003315131,0.9957178,0.0006189188,0.001190551,0.001742136,0.000006451632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1174684,0.0009150355,0.8335711,0.0005784369,0.0001864277,0.0001670236,0.0002198017,0.001628006,0.0452657],"genre_scores_gemma":[0.8532128,0.0003016599,0.1280459,0.0001873002,0.00002998516,0.0001281407,0.0003976082,0.0002026937,0.01749393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1299662,"threshold_uncertainty_score":0.2584194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003227075735683697,"score_gpt":0.1668420923968778,"score_spread":0.163615016661194,"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."}}