{"id":"W4385068553","doi":"10.3390/w15142641","title":"Intensity–Duration–Frequency Curves for Dependent Datasets","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precipitation; Multivariate statistics; Univariate; Range (aeronautics); Statistics; Generalized extreme value distribution; Duration (music); Maxima; Intensity (physics); Environmental science; Independence (probability theory); Extreme value theory; Mathematics; Climatology; Econometrics; Meteorology; Geography; Geology; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01494639,0.0006510327,0.0006133754,0.004029157,0.000447137,0.001629429,0.001429408,0.001509282,0.006764992],"category_scores_gemma":[0.06138026,0.000231638,0.001707243,0.003997103,0.0007466837,0.002715789,0.00143918,0.001868811,0.002078867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001100419,"about_ca_system_score_gemma":0.0008808068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004810383,"about_ca_topic_score_gemma":0.003928574,"domain_scores_codex":[0.9955621,0.001716544,0.0004101898,0.001226571,0.0007864363,0.0002981693],"domain_scores_gemma":[0.9606938,0.0242019,0.002976404,0.009753576,0.001954936,0.0004195249],"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":[0.001157637,0.0004437472,0.21049,0.001185099,0.000873786,0.0009608498,0.001502146,0.3507391,0.008370944,0.08637417,0.03975717,0.2981454],"study_design_scores_gemma":[0.00004817827,0.0002792078,0.1272799,0.0001504999,0.00009195603,0.001154377,0.0006158074,0.7550895,0.00342671,0.05121487,0.06052285,0.0001261761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1973993,0.0009777085,0.7324226,0.0009681148,0.0001718596,0.0007433759,0.04852417,0.008030559,0.01076226],"genre_scores_gemma":[0.7540691,0.0004557304,0.1917203,0.000250661,0.0001326116,0.001050316,0.0477395,0.001087445,0.003494265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01494639,"threshold_uncertainty_score":0.079045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686004010512766,"score_gpt":0.251025640871878,"score_spread":0.2341656007667504,"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."}}