{"id":"W4221091645","doi":"10.5194/egusphere-egu22-1510","title":"From virtual environment to real observations: short-term hydrological forecasts with an Artificial Neural Network model.","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Streamflow; Watershed; Environmental science; Precipitation; Artificial neural network; Hydrology (agriculture); Hydrological modelling; Stream flow; Antecedent moisture; Meteorology; Computer science; Climatology; Geography; Machine learning; Cartography; Runoff curve number; Geology; Drainage basin","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":[0.0004748747,0.0004242148,0.0002883526,0.0002623956,0.0002069163,0.0007841176,0.0008102333,0.0009028612,0.001630463],"category_scores_gemma":[0.00211606,0.0002907244,0.0003576811,0.0003925382,0.0002952734,0.001085071,0.0005394855,0.0008018092,0.0003734391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005975381,"about_ca_system_score_gemma":0.0005967435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01709868,"about_ca_topic_score_gemma":0.01710907,"domain_scores_codex":[0.9998536,0.00004965529,0.000009668714,0.00003830445,0.00003683068,0.00001189842],"domain_scores_gemma":[0.9995632,0.0002179942,0.00005671728,0.00004408384,0.0000842505,0.00003374103],"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":[0.00006509405,0.00003684853,0.001465924,0.00002317775,0.00002225168,0.0000577365,0.00002413313,0.9794897,0.0005453167,0.0009527691,0.001170874,0.01614625],"study_design_scores_gemma":[0.000003317472,0.000005618493,0.0002522056,0.000002086002,0.000001811472,0.000002817992,0.000003770916,0.9989115,0.0001387502,0.0004240369,0.0002517533,0.000002299643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3753802,0.0008454898,0.5997669,0.001399431,0.0005432114,0.0001545289,0.003828105,0.002869044,0.01521298],"genre_scores_gemma":[0.9410053,0.0002279224,0.05311164,0.0000875537,0.00004357573,0.00009306845,0.001443613,0.00005993932,0.003927457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01709868,"threshold_uncertainty_score":0.03399825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06115413471638774,"score_gpt":0.2554315152670815,"score_spread":0.1942773805506937,"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."}}