{"id":"W1886827333","doi":"10.1029/2005wr004397","title":"A parametric Bayesian combination of local and regional information in flood frequency analysis","year":2006,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Estimator; Bayesian probability; Statistics; Mathematics; Econometrics; Computer science","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":[0.006227513,0.0007094273,0.001314426,0.002161252,0.0004476236,0.001494383,0.001493267,0.00101225,0.00180899],"category_scores_gemma":[0.01838296,0.0009344545,0.001096479,0.001860928,0.0008031716,0.002642373,0.002542838,0.001201261,0.0005707178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004957698,"about_ca_system_score_gemma":0.001064632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003511724,"about_ca_topic_score_gemma":0.004889807,"domain_scores_codex":[0.9969034,0.001596722,0.00013513,0.0004463291,0.0007731092,0.0001453801],"domain_scores_gemma":[0.9953713,0.003117222,0.0004190639,0.0004524431,0.000520807,0.0001191346],"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":[0.000235924,0.0001117898,0.007157289,0.0002113201,0.0003255498,0.0001911744,0.0002184777,0.5004768,0.003873211,0.03972235,0.001704528,0.4457715],"study_design_scores_gemma":[0.00001540002,0.00003733382,0.002266825,0.0000351946,0.00007045767,0.0001165064,0.00002696846,0.9695207,0.0009043154,0.02532564,0.001625428,0.00005515191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005997109,0.0002468532,0.9929793,0.00004055971,0.00000621335,0.00001856909,0.00005272697,0.0001379077,0.0005207172],"genre_scores_gemma":[0.324845,0.0008564822,0.6706308,0.0001215955,0.0001440235,0.0001956983,0.0006160641,0.0002045906,0.002385854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006227513,"threshold_uncertainty_score":0.03293461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289218017975633,"score_gpt":0.2620440295927272,"score_spread":0.2491518494129708,"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."}}