{"id":"W2787789979","doi":"10.1109/pesgm.2017.8274641","title":"Condition assessment and failure probability of existing transmission lines","year":2017,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; McGill University; Hydro One (Canada)","funders":"","keywords":"Electric power transmission; Reliability engineering; Random variable; Cumulative distribution function; Computer science; Margin (machine learning); Fuzzy logic; Probability density function; Probability distribution; Failure rate; Invariant (physics); Maintenance engineering; Engineering; Mathematics; Statistics; Electrical engineering; Artificial intelligence","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":[0.0005271708,0.0002907557,0.0002141261,0.001293858,0.0001773984,0.0004026078,0.0003287171,0.0004872514,0.001338941],"category_scores_gemma":[0.003737194,0.0001415707,0.0002175229,0.0004170281,0.0003148669,0.0007719307,0.000215871,0.0002419907,0.000168633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004031765,"about_ca_system_score_gemma":0.0001649243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790453,"about_ca_topic_score_gemma":0.002639161,"domain_scores_codex":[0.9995992,0.00006021828,0.00002397423,0.000105824,0.0001724139,0.0000384907],"domain_scores_gemma":[0.9985889,0.0006805654,0.0002537436,0.00009567755,0.0003274655,0.00005364563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005629265,0.000144325,0.1425953,0.0001182261,0.00009401418,0.0007142904,0.0003105402,0.6481399,0.06034546,0.003188971,0.0004203999,0.1433657],"study_design_scores_gemma":[0.00001296109,0.0002261072,0.06660302,0.00001013417,0.00003648646,0.0002204631,0.00006378486,0.9170754,0.01415462,0.001147742,0.0004170423,0.00003228774],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8813731,0.0001675689,0.1164308,0.00002340172,0.000005452988,0.00001918542,0.0001400304,0.0002613998,0.001579033],"genre_scores_gemma":[0.996518,0.00003211354,0.003140393,0.0000010367,0.000002368614,0.000004317623,0.00005556686,0.000004208869,0.0002420048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003790453,"threshold_uncertainty_score":0.007536769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555644082888229,"score_gpt":0.3066358085971662,"score_spread":0.2810793677682839,"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."}}