{"id":"W1963796430","doi":"10.1175/jcli3366.1","title":"Avoiding Inhomogeneity in Percentile-Based Indices of Temperature Extremes","year":2005,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":511,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Institute for Climate Solutions","funders":"U.S. Department of Energy; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Percentile; Resampling; Classification of discontinuities; Environmental science; Monte Carlo method; Statistics; Base (topology); Climatology; Period (music); Return period; Climate change; Sampling (signal processing); Meteorology; Mathematics; Computer science; Geology; Geography","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":[0.005258779,0.000222155,0.0005134798,0.0008455326,0.0003083251,0.000852362,0.0005699685,0.0003927108,0.0004132493],"category_scores_gemma":[0.02674651,0.0002749554,0.0003016508,0.0008867651,0.0005271251,0.0008823112,0.0009612115,0.0006655215,0.000136898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005293647,"about_ca_system_score_gemma":0.0007161921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003034497,"about_ca_topic_score_gemma":0.00301666,"domain_scores_codex":[0.9984099,0.0009110176,0.00007609159,0.0001697345,0.0003260725,0.000107187],"domain_scores_gemma":[0.9861401,0.009418703,0.00146061,0.001859666,0.0009038103,0.0002171022],"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":[0.0003688634,0.00006910574,0.06564018,0.00006103128,0.0001559301,0.0001269852,0.0002043661,0.8203663,0.007319306,0.01797399,0.0006059885,0.08710807],"study_design_scores_gemma":[0.000009174566,0.00005863219,0.01417622,0.00001200766,0.00001580824,0.00008416916,0.00002060183,0.9732512,0.002936305,0.008949841,0.0004655613,0.00002046698],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4411539,0.0001279888,0.5564929,0.00008341527,0.00001576618,0.00003594513,0.0001082022,0.000454417,0.001527533],"genre_scores_gemma":[0.9495396,0.00002850262,0.05004903,0.00001639024,0.00001030935,0.00002248782,0.0001565824,0.00003926739,0.0001378668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005258779,"threshold_uncertainty_score":0.02781147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160161813411765,"score_gpt":0.2569855099546814,"score_spread":0.240969328613505,"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."}}