{"id":"W2093976847","doi":"10.1080/02626660209492909","title":"The use of flood regime information in regional flood frequency analysis","year":2002,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Flood myth; Quantile; Pooling; 100-year flood; Environmental science; Resampling; Return period; Flood forecasting; Hydrology (agriculture); Statistics; Computer science; Geography; Mathematics; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002664215,0.0003261875,0.0004554613,0.00209814,0.0001842437,0.0004675662,0.0002797137,0.0002054427,0.0005317782],"category_scores_gemma":[0.006973358,0.0001576538,0.0003754015,0.001341844,0.0002231958,0.0009003943,0.000791452,0.0002297287,0.0001341226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001935085,"about_ca_system_score_gemma":0.0002250056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001759801,"about_ca_topic_score_gemma":0.001939756,"domain_scores_codex":[0.9994161,0.0002790327,0.00004322012,0.0001147461,0.00009831541,0.00004860722],"domain_scores_gemma":[0.9973676,0.001480034,0.0004282491,0.0003944578,0.0002340605,0.00009560665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001358547,0.0001936307,0.2335381,0.0001667576,0.0007784496,0.0002519087,0.0006812642,0.1094449,0.05170453,0.002031491,0.0008302825,0.5990202],"study_design_scores_gemma":[0.00004534823,0.0006368301,0.436605,0.00003592678,0.0003132841,0.0002531373,0.0003098301,0.5379323,0.01786171,0.004565629,0.001343073,0.00009783986],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8084055,0.0001424186,0.1894609,0.00003386165,0.000007947296,0.00006143839,0.0004273003,0.0003435092,0.001117211],"genre_scores_gemma":[0.9699276,0.00003752149,0.02952205,0.000007458407,0.00001405133,0.00002547038,0.0003439728,0.0000174882,0.0001043295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002664215,"threshold_uncertainty_score":0.01408982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04670953399902472,"score_gpt":0.2393498686924196,"score_spread":0.1926403346933949,"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."}}