{"id":"W3107733674","doi":"10.1175/jhm-d-20-0053.1","title":"On the Value of River Network Information in Regional Frequency Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Mean squared error; Streamflow; Quantile; Statistics; Jackknife resampling; Computer science; Artificial neural network; Environmental science; Hydrology (agriculture); Mathematics; Drainage basin; Estimator; Geology; Artificial intelligence","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":[0.009582583,0.0007108485,0.000739388,0.001782369,0.0003957564,0.001569193,0.0005997642,0.0007378788,0.0004285615],"category_scores_gemma":[0.03334571,0.0002920487,0.0005734819,0.001605123,0.0005749122,0.002410684,0.001071132,0.001030265,0.0001312166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006196006,"about_ca_system_score_gemma":0.000792205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007726214,"about_ca_topic_score_gemma":0.006941454,"domain_scores_codex":[0.9974381,0.001737281,0.0001108572,0.0003338006,0.0003018083,0.00007818176],"domain_scores_gemma":[0.9707085,0.02430279,0.001163681,0.001582833,0.0020356,0.0002065507],"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":[0.0003283016,0.0001151921,0.09969991,0.00006260017,0.0003974251,0.0001074688,0.00009374252,0.76261,0.002215504,0.002613358,0.0005976037,0.1311589],"study_design_scores_gemma":[0.000006149477,0.00003717721,0.007767559,0.00001917376,0.00004069962,0.00001414808,0.00002124519,0.9898403,0.0007382876,0.001318336,0.0001814995,0.00001551781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6055424,0.001265738,0.3884769,0.0008886713,0.00008517854,0.00007352583,0.0003782936,0.0004664077,0.002822929],"genre_scores_gemma":[0.9696537,0.0001595834,0.02979705,0.00003356854,0.00003016639,0.00001350874,0.0001083247,0.00002543952,0.0001786823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009582583,"threshold_uncertainty_score":0.05067813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009688978905873946,"score_gpt":0.211004308383056,"score_spread":0.201315329477182,"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."}}