{"id":"W4280556630","doi":"10.1029/2021wr031641","title":"Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling","year":2022,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Grantová Agentura České Republiky","keywords":"Copula (linguistics); Statistical physics; Series (stratigraphy); Mathematics; Rank correlation; Probability and statistics; Exponential function; Applied mathematics; Statistics; Geology; Econometrics; Physics; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002864899,0.0001253398,0.0001882196,0.0001875682,0.001282669,0.0001014251,0.0002874479,0.00004678886,0.004817513],"category_scores_gemma":[0.00007246157,0.0001003064,0.00005088887,0.0003236555,0.0001998863,0.0002173599,0.001042848,0.0004819416,0.0002377227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002347586,"about_ca_system_score_gemma":0.000006703997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004890894,"about_ca_topic_score_gemma":0.00008976833,"domain_scores_codex":[0.9972011,0.000764656,0.0002534468,0.000541686,0.0006807265,0.0005584016],"domain_scores_gemma":[0.9994484,0.00004554316,0.00002905429,0.0003412668,0.00002155496,0.0001141483],"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":[0.0002182849,0.00007172264,0.02021345,0.00002532068,0.00004089342,0.00007127187,0.007742194,0.7801919,0.1828299,0.000007908935,0.00349997,0.005087148],"study_design_scores_gemma":[0.0002478361,0.0001646339,0.0001306691,0.000007300073,0.00001436054,0.00001809409,0.0003223434,0.9569106,0.004301446,0.0001953898,0.03752559,0.0001617632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955428,0.0001318225,0.0008504809,0.002146789,0.00002334317,0.0001968694,0.000004670969,0.00003526722,0.00106794],"genre_scores_gemma":[0.9953452,0.00001110819,0.0006811456,0.0003034394,0.000111293,0.00006403386,0.00008087442,0.00001920977,0.003383651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1785285,"threshold_uncertainty_score":0.9960922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02460384749259363,"score_gpt":0.2796623460145745,"score_spread":0.2550584985219808,"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."}}