{"id":"W4377013723","doi":"10.1175/jhm-d-22-0224.1","title":"Temporal and Spatial Amplification of Extreme Rainfall and Extreme Floods in a Warmer Climate","year":2023,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Leibniz-Gemeinschaft; Environment and Climate Change Canada; Bayerische Akademie der Wissenschaften; Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst; Gauss Centre for Supercomputing; Bundesministerium für Bildung und Forschung; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Leibniz-Rechenzentrum; Université du Québec à Montréal","keywords":"Environmental science; Streamflow; Precipitation; Impervious surface; Drainage basin; Return period; Climatology; Climate change; STREAMS; Hydrology (agriculture); Flood myth; Meteorology; Geology; Geography","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":[0.000403696,0.00008691265,0.0001383207,0.0003213226,0.0002072053,0.0003767973,0.00008639124,0.0001792104,0.0009280073],"category_scores_gemma":[0.0008371893,0.0001249881,0.0001482156,0.0003457064,0.0002846487,0.0003297583,0.0003897981,0.0002029376,0.00006469108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002428267,"about_ca_system_score_gemma":0.0001279197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004772359,"about_ca_topic_score_gemma":0.01055681,"domain_scores_codex":[0.9998928,0.00002961876,0.000004692267,0.00002777396,0.00002042318,0.00002470352],"domain_scores_gemma":[0.9995254,0.0001838289,0.0001462767,0.00003953683,0.00004952201,0.00005527462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001809878,0.0000637686,0.9831229,0.00001159728,0.00005548851,0.00009289919,0.0001976406,0.006839865,0.004538532,0.0001291864,0.0001693172,0.004597841],"study_design_scores_gemma":[0.000001587879,0.00001524697,0.9971998,7.038377e-7,0.000004557836,0.00001633826,0.00006498761,0.002445006,0.0001494686,0.00002871091,0.00007161062,0.000002053805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997361,0.000004932284,0.00007714928,0.000007430558,4.245523e-7,6.133145e-7,0.00004309795,0.000004111649,0.0001261296],"genre_scores_gemma":[0.999801,0.000005467555,0.00006407579,0.000002456836,0.000002181615,0.000001538443,0.00007731921,9.609589e-7,0.00004521091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004772359,"threshold_uncertainty_score":0.009489179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02769482119651222,"score_gpt":0.2419749482053936,"score_spread":0.2142801270088814,"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."}}