{"id":"W3139034298","doi":"","title":"Application of hydrometeorological indices for hydrologic forecasts within an artificial neural network framework","year":2020,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Hydrometeorology; Artificial neural network; Environmental science; Hydrological modelling; Meteorology; Climatology; Computer science; Hydrology (agriculture); Artificial intelligence; Geology; Precipitation; Geography","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":[0.0007830675,0.0003394129,0.0003186126,0.0004309081,0.000255869,0.0008134414,0.0004853316,0.0004799311,0.0008671928],"category_scores_gemma":[0.001962282,0.0001578489,0.0002542468,0.0005005402,0.0002156852,0.0006810744,0.0003883694,0.0005725867,0.0001402307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004906107,"about_ca_system_score_gemma":0.0006786641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01486992,"about_ca_topic_score_gemma":0.0117954,"domain_scores_codex":[0.9998295,0.00005493539,0.00001308512,0.00003499323,0.00005107895,0.00001632071],"domain_scores_gemma":[0.9996543,0.0001533883,0.00004096848,0.0000158589,0.000118499,0.00001696079],"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":[0.00003615284,0.00003960643,0.001855397,0.00001843026,0.00003688129,0.00003426762,0.00001708549,0.9487219,0.001863215,0.004569262,0.00036527,0.04244244],"study_design_scores_gemma":[9.599896e-7,0.000002607895,0.0001676245,9.687302e-7,0.00000213413,0.000001211767,0.000001322805,0.9990591,0.0001403148,0.0005550123,0.0000674597,0.000001283747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1740845,0.0006096485,0.8142715,0.000717212,0.000198512,0.00006136304,0.0002463621,0.0003875598,0.009423359],"genre_scores_gemma":[0.9289154,0.0002459953,0.06832054,0.00004110507,0.0001034337,0.00003482389,0.0001369252,0.00002747388,0.00217432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01486992,"threshold_uncertainty_score":0.02956676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04450727962414262,"score_gpt":0.2686842983142699,"score_spread":0.2241770186901273,"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."}}