{"id":"W4321501219","doi":"10.5194/egusphere-egu23-2950","title":"Evaluation of continental-scale ensemble hydrological forecasts from Environment and Climate Change Canada: a comparison with forecasts from the Global Flood Awareness System (GloFAS)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Environment and Climate Change Canada","funders":"","keywords":"Streamflow; Flood forecasting; Environmental science; Flood myth; Climatology; Climate change; Watershed; Scale (ratio); Meteorology; Earth system science; Environmental resource management; Geography; Drainage basin; Computer science; Cartography; Geology; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002971692,0.0008884731,0.0004798476,0.0009764085,0.0005611021,0.001413428,0.001148671,0.0005588982,0.0008940967],"category_scores_gemma":[0.006172627,0.0001966692,0.0004485163,0.001106045,0.0002880399,0.0009155265,0.0008374757,0.0006486338,0.0001746604],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007122022,"about_ca_system_score_gemma":0.007675115,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8737503,"about_ca_topic_score_gemma":0.8422344,"domain_scores_codex":[0.9989707,0.0001564264,0.00006189077,0.0001752551,0.000520666,0.0001150629],"domain_scores_gemma":[0.9959305,0.0008782598,0.0002463443,0.0002470545,0.002354624,0.000343236],"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":[0.001551112,0.0005401742,0.1476567,0.0001756329,0.0007069177,0.0002420237,0.0002218767,0.7698893,0.002758726,0.00107285,0.007868409,0.06731635],"study_design_scores_gemma":[0.0001853309,0.0002874403,0.1022411,0.00003695472,0.0001038222,0.00003129377,0.0002746641,0.8913196,0.002175029,0.0002011596,0.003083678,0.00005987089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815301,0.000485425,0.001934638,0.0004971939,0.00006751891,0.00009973662,0.008394034,0.0004426261,0.006548646],"genre_scores_gemma":[0.9812257,0.0002727621,0.003534049,0.00008357834,0.00002266607,0.00002928302,0.01374468,0.00003608564,0.001051184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.992878,"threshold_uncertainty_score":0.2539864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05990241785469957,"score_gpt":0.2558189885119314,"score_spread":0.1959165706572318,"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."}}