{"id":"W4411731065","doi":"10.1016/j.jhydrol.2025.133789","title":"Climatic a priori information for the GEV distribution’s shape parameter of annual maximum flow series","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; University of Sydney","keywords":"Series (stratigraphy); A priori and a posteriori; Flow (mathematics); Distribution (mathematics); Mathematics; Meteorology; Climatology; Statistical physics; Environmental science; Geology; Geography; Geometry; Physics; Mathematical analysis; Philosophy","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":[0.002839802,0.0005677966,0.0006780961,0.001205704,0.0003597306,0.0009820645,0.0008359493,0.001187608,0.006974646],"category_scores_gemma":[0.01276577,0.000833704,0.0009335598,0.0008302286,0.0004869908,0.002892788,0.0004828935,0.001223883,0.001270213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005877802,"about_ca_system_score_gemma":0.0006168593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056088,"about_ca_topic_score_gemma":0.003329104,"domain_scores_codex":[0.9995447,0.0001658646,0.00003234865,0.0001403082,0.00005604851,0.00006086922],"domain_scores_gemma":[0.9938807,0.004054012,0.0005100937,0.0009848455,0.0003975386,0.0001727765],"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":[0.0008831146,0.0001288172,0.1013047,0.0002337554,0.0003655167,0.0005221267,0.0001308413,0.8019956,0.0187892,0.03052332,0.006709543,0.03841355],"study_design_scores_gemma":[0.00006935758,0.00007737427,0.1578928,0.0001020118,0.00008019391,0.0003725341,0.00005584126,0.7997596,0.003649742,0.03452068,0.003311229,0.0001087325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6621397,0.001113992,0.3024274,0.001864172,0.0002931325,0.00004896701,0.01813751,0.001329152,0.01264585],"genre_scores_gemma":[0.9889309,0.0002518543,0.004784246,0.00009282606,0.0001370462,0.00001699568,0.005029572,0.0001193324,0.0006373503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006974646,"threshold_uncertainty_score":0.02333248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005705991540492805,"score_gpt":0.2142584145502051,"score_spread":0.2085524230097123,"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."}}