{"id":"W2143430740","doi":"10.1109/ccece.1999.804874","title":"Modelling extreme-weather-related transmission line outages","year":2003,"lang":"en","type":"article","venue":"","topic":"Thermal Analysis in Power Transmission","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Electric power transmission; Transmission line; Reliability (semiconductor); Storm; Meteorology; Icing; Electricity; Transmission (telecommunications); Extreme weather; Monte Carlo method; Computer science; Line (geometry); Lightning (connector); Power transmission; Wind power; Environmental science; Reliability engineering; Telecommunications; Power (physics); Engineering; Geography; Electrical engineering; Statistics; Climate change","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001688564,0.0002103061,0.0002069664,0.0001034139,0.00006216065,0.00002216,0.0001308448,0.0001382246,0.003001747],"category_scores_gemma":[0.000003475103,0.0001684072,0.0001442794,0.0002548198,0.00001713808,0.0001196298,0.000003584601,0.0002079456,0.0001283253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002883493,"about_ca_system_score_gemma":0.000007108833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008026947,"about_ca_topic_score_gemma":8.872439e-7,"domain_scores_codex":[0.9989751,0.00003825537,0.0003242748,0.0002047001,0.0001845397,0.0002731564],"domain_scores_gemma":[0.9995648,0.00002908324,0.00001565859,0.0002325687,0.00002390582,0.0001340341],"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":[0.000003528764,0.00002932668,0.00002466948,0.00002280276,0.00006018193,0.000005769792,0.0003056561,0.9651579,0.02269293,0.0009343899,0.00009283338,0.01066995],"study_design_scores_gemma":[0.0003144688,0.00001478814,0.000003797825,0.00004304271,0.00005113841,0.000005419426,0.00004252612,0.9150925,0.04655421,0.001451939,0.03617202,0.0002541596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03672628,0.001741722,0.9047998,0.00005860929,0.0001445707,0.00008571474,0.000001236307,0.000724162,0.05571791],"genre_scores_gemma":[0.9779731,0.0003660923,0.01587684,0.00002130369,0.00001517514,0.000004117034,0.000005925809,0.00005944412,0.005677982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9412468,"threshold_uncertainty_score":0.9979097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02052506649793668,"score_gpt":0.2088479183579921,"score_spread":0.1883228518600555,"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."}}