{"id":"W4391130394","doi":"10.5194/egusphere-2023-3040","title":"FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Alberta; Environment and Climate Change Canada; Ouranos; University of Saskatchewan; Alberta Environment and Protected Areas; University of Calgary","funders":"Global Water Futures; National Oceanic and Atmospheric Administration; Canada First Research Excellence Fund; Environment and Climate Change Canada; Deltares","keywords":"Streamflow; Snow; Probabilistic logic; Workflow; Climatology; Environmental science; Hydrology (agriculture); Water year; Flood forecasting; Drainage basin; Geography; Meteorology; Geology; Cartography; Computer science; Database; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.003032417,0.001501605,0.0006344652,0.001063695,0.000885291,0.00162917,0.002360878,0.0005660216,0.005670938],"category_scores_gemma":[0.006888907,0.0007776831,0.001152298,0.0008332522,0.0006153762,0.001475415,0.001947802,0.001317531,0.002412103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001507017,"about_ca_system_score_gemma":0.006107795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06786127,"about_ca_topic_score_gemma":0.06614149,"domain_scores_codex":[0.9991036,0.0001703868,0.00009270029,0.000322279,0.0002167872,0.0000943003],"domain_scores_gemma":[0.9976195,0.0007039605,0.0001325057,0.0005794836,0.0006288646,0.0003356949],"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":[0.002292572,0.0007837359,0.03152565,0.0009606958,0.0006926688,0.001237624,0.002780912,0.3427462,0.02867603,0.01628784,0.254248,0.317768],"study_design_scores_gemma":[0.0004140299,0.00009573124,0.004931346,0.00007407074,0.00003490083,0.00006968329,0.0001875501,0.9291192,0.00823682,0.007130413,0.04958114,0.0001250538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04693301,0.0002378463,0.4906477,0.0005698551,0.0002086377,0.001317703,0.02229433,0.4302165,0.007574468],"genre_scores_gemma":[0.3330773,0.0004121511,0.5793257,0.0003604221,0.00005355552,0.001883899,0.06151829,0.01795413,0.005414378],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06786127,"threshold_uncertainty_score":0.1349325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09083418553985674,"score_gpt":0.2871590186275236,"score_spread":0.1963248330876669,"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."}}