{"id":"W4205412075","doi":"10.1049/gtd2.12390","title":"Forecast of transmission line clearance using quantile regression‐based weather forecasts","year":2022,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Thermal Analysis in Power Transmission","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile regression; Quantile; Transmission line; Regression; Weather forecasting; Regression analysis; Meteorology; Environmental science; Econometrics; Statistics; Computer science; Mathematics; Geography; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008867807,0.0003046699,0.0002708203,0.0003804189,0.0001058172,0.0004699895,0.0003539987,0.0002357068,0.0008306634],"category_scores_gemma":[0.002416192,0.0001836364,0.0002835477,0.000425454,0.0001399045,0.0004264364,0.0001844564,0.0004015093,0.0002022141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004277399,"about_ca_system_score_gemma":0.0002732911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01152545,"about_ca_topic_score_gemma":0.006507247,"domain_scores_codex":[0.9997324,0.00009355322,0.00001147607,0.00005816263,0.00007374797,0.00003057251],"domain_scores_gemma":[0.9990535,0.0004190345,0.000193794,0.00008597467,0.0002172542,0.0000303947],"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":[0.00003445311,0.00001305183,0.004652277,0.00000634294,0.000009971245,0.0000156179,0.000005444541,0.9880897,0.0007890741,0.0004686057,0.0002139739,0.005701367],"study_design_scores_gemma":[0.000001530898,0.000005514232,0.001249016,7.964372e-7,0.000001003403,0.000001504751,0.000001050844,0.9984562,0.0001468929,0.0001072442,0.00002782673,0.000001319279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5978113,0.0001247391,0.3977259,0.0001619543,0.00002878455,0.00002536986,0.0008281022,0.0007588302,0.002534999],"genre_scores_gemma":[0.9958528,0.00002501758,0.003703679,0.000003682278,0.000004621718,0.000004803875,0.0002155985,0.000008573376,0.0001812861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01152545,"threshold_uncertainty_score":0.02291673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02871491471624782,"score_gpt":0.2541731109471888,"score_spread":0.225458196230941,"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."}}