{"id":"W2786012603","doi":"10.1109/pesgm.2017.8274539","title":"On-line loading limit monitoring and reliability must-run development using artificial neural networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Hydro (Canada); University of British Columbia","funders":"Rocky Mountain Research Station; BC Hydro","keywords":"Reliability (semiconductor); Artificial neural network; Computer science; Limit (mathematics); Margin (machine learning); Process (computing); Reliability engineering; Voltage; Line (geometry); Artificial intelligence; Control engineering; Engineering; Machine learning; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0006192892,0.0006344211,0.0003547866,0.0007565352,0.0002192279,0.0005474362,0.0004681612,0.000314144,0.0006350844],"category_scores_gemma":[0.002349108,0.0002164014,0.0001904705,0.0004519973,0.0001912593,0.0005660926,0.0003246203,0.0003749057,0.0001416079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005556358,"about_ca_system_score_gemma":0.0003475611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006417419,"about_ca_topic_score_gemma":0.01283395,"domain_scores_codex":[0.9995316,0.00013278,0.00003733117,0.00006847146,0.0001985737,0.00003122642],"domain_scores_gemma":[0.9989012,0.0005140904,0.0002181445,0.00006697064,0.0002755365,0.00002392119],"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":[0.0001738391,0.00009759806,0.01334395,0.0000974987,0.00005484537,0.0001316232,0.000104258,0.5861078,0.009140861,0.001003594,0.001008229,0.3887359],"study_design_scores_gemma":[0.000002804979,0.00003090213,0.002333558,0.000009742212,0.000006786576,0.00002237362,0.00001177335,0.9937769,0.003072954,0.00040812,0.0003170014,0.000007041258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2345359,0.0004389671,0.7542076,0.0002350096,0.00003787344,0.0000808117,0.0001226513,0.001682253,0.008658849],"genre_scores_gemma":[0.9444213,0.0001212233,0.05368548,0.00002958804,0.00001361094,0.00003584878,0.00007635227,0.00003498076,0.001581537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006417419,"threshold_uncertainty_score":0.01276016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05115405114736744,"score_gpt":0.2764507533154594,"score_spread":0.225296702168092,"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."}}