{"id":"W3178844591","doi":"10.1029/2020ms002447","title":"Evaluating Precipitation Errors Using the Environmentally Conditioned Intensity‐Frequency Decomposition Method","year":2021,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precipitation; Forcing (mathematics); Compensation (psychology); Intensity (physics); Convection; Parameterized complexity; Computer science; Environmental science; Meteorology; Algorithm; Atmospheric sciences; Geology; Physics; Optics","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":[],"consensus_categories":[],"category_scores_codex":[0.001640582,0.00009241838,0.0002376581,0.00008706991,0.0001887492,0.00006101314,0.0001123946,0.00004548366,0.0001273638],"category_scores_gemma":[0.0001874523,0.00006286587,0.00007128079,0.0001784301,0.00002925757,0.0006512265,0.000007313694,0.0002172238,0.000004265034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001825403,"about_ca_system_score_gemma":0.00005689986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006610656,"about_ca_topic_score_gemma":0.0001081953,"domain_scores_codex":[0.9980559,0.0005729877,0.0006791204,0.0001401758,0.0003995019,0.0001523157],"domain_scores_gemma":[0.9988974,0.0003727477,0.000392358,0.0001043628,0.000179039,0.00005408428],"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":[0.00003043571,0.00001173904,0.01097586,0.00001177567,0.00001134864,0.00001181767,0.0002727178,0.9827528,0.003501696,0.0000584145,3.143815e-7,0.002361071],"study_design_scores_gemma":[0.0002808795,0.0001269756,0.005059477,0.0001001463,0.00002519252,0.000145249,0.001238194,0.9871033,0.00005590377,0.005773483,0.000015197,0.0000759438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7366146,0.004111819,0.2583032,0.00005772056,0.0005728549,0.0001060841,0.000006667256,0.000004031229,0.0002230312],"genre_scores_gemma":[0.9273327,0.0001451746,0.07228542,0.00006935485,0.0001369851,5.322686e-7,0.00001903193,0.000002602103,0.000008221631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1907181,"threshold_uncertainty_score":0.2563596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0817696835426389,"score_gpt":0.3708364008333777,"score_spread":0.2890667172907388,"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."}}