{"id":"W2601899948","doi":"10.5194/hess-21-1651-2017","title":"Heterogeneity measures in hydrological frequency analysis: review and new developments","year":2017,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l'Éducation et de l'Enseignement supérieur","keywords":"Quantile; Homogeneity (statistics); Statistics; Ranking (information retrieval); Monte Carlo method; Context (archaeology); Spatial heterogeneity; Econometrics; Gini coefficient; Homogeneous; Computer science; Mathematics; Geography; Inequality","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":[0.001887729,0.0001706073,0.0005363238,0.0001132604,0.0009555176,0.00006543141,0.0004569811,0.0001582221,0.0001729977],"category_scores_gemma":[0.0001009772,0.0001252146,0.00006587523,0.0003637286,0.001293896,0.0003477802,0.000263371,0.0001373487,0.0001032629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001438546,"about_ca_system_score_gemma":0.0000233602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001677918,"about_ca_topic_score_gemma":0.008661306,"domain_scores_codex":[0.9981297,0.0002564676,0.0003399532,0.0006690177,0.00021465,0.0003902153],"domain_scores_gemma":[0.9992433,0.00005010837,0.0002002337,0.0003244327,0.000004907452,0.0001770285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004923686,0.00001128196,0.9978507,0.00002201057,0.00007363378,0.00002410017,0.0000655937,0.000225437,0.00004238619,0.0002193984,0.00003203974,0.00142845],"study_design_scores_gemma":[0.000236407,0.00008978703,0.9943628,0.00004261332,0.0003880967,0.00007262468,0.00001721577,0.003442741,0.00003231595,0.000639413,0.0004745346,0.0002014256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826382,0.005916198,0.0001043732,0.001481768,0.00004969354,0.0001489253,0.000001360455,0.00003268211,0.009626774],"genre_scores_gemma":[0.9975672,0.001097379,0.000500461,0.0007121885,0.00001358194,0.00001172265,0.000001128566,0.000002922156,0.00009337298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01492902,"threshold_uncertainty_score":0.7349166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04271403988173701,"score_gpt":0.2795795383791747,"score_spread":0.2368654984974377,"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."}}