{"id":"W2051024048","doi":"10.1139/l00-122","title":"Flood frequency analysis for the Red River at Winnipeg","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flood myth; Quantile; 100-year flood; Return period; Series (stratigraphy); Environmental science; Magnitude (astronomy); Hydrology (agriculture); Time series; Geography; Statistics; Mathematics; Geology; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003277961,0.0001339609,0.00016615,0.0006482744,0.0002476238,0.0003182695,0.0002692072,0.0001321406,0.0004265344],"category_scores_gemma":[0.001925532,0.0001151462,0.0001866385,0.000808022,0.0001585295,0.0001907128,0.0002771351,0.0001785876,0.00003643299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179949,"about_ca_system_score_gemma":0.001095908,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4947837,"about_ca_topic_score_gemma":0.5219632,"domain_scores_codex":[0.9998901,0.00002712996,0.000007087681,0.0000195205,0.00003768846,0.00001849949],"domain_scores_gemma":[0.9997103,0.0001336074,0.00004176419,0.0000205624,0.0000767494,0.00001706374],"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":[0.0002055072,0.0000705676,0.336621,0.0001370565,0.0001957211,0.000734007,0.0008699434,0.5914008,0.005385448,0.007302333,0.001515594,0.05556199],"study_design_scores_gemma":[0.00003370789,0.00007513908,0.41447,0.00003059923,0.00006365973,0.0002720321,0.0005617855,0.5754009,0.002431229,0.00162103,0.004981774,0.00005814344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994551,0.0001340665,0.003837858,0.00007191435,0.000003171597,0.00001897344,0.0005147286,0.0000228138,0.0008454903],"genre_scores_gemma":[0.995114,0.0001802699,0.003507874,0.000007719574,0.000001912491,0.00001399059,0.0007551351,0.000008054471,0.0004110539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5052164,"threshold_uncertainty_score":0.9838074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006944577926555206,"score_gpt":0.1804851642606658,"score_spread":0.1735405863341106,"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."}}