{"id":"W4321481323","doi":"10.5194/egusphere-egu23-5736","title":"A Novel Workflow for Streamflow Prediction in the Presence of Missing Gauge Observations","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Streamflow; Missing data; Imputation (statistics); Categorical variable; Computer science; Flood myth; Flood forecasting; Leverage (statistics); Environmental science; Climatology; Data mining; Artificial intelligence; Machine learning; Cartography; Drainage basin; 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":[0.002049433,0.0009483281,0.0006733869,0.0009208916,0.0008049997,0.00163747,0.001865258,0.0006745601,0.004772621],"category_scores_gemma":[0.004306894,0.0004846545,0.001116244,0.000937207,0.0004407149,0.001830621,0.001767697,0.001160881,0.002852733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006580721,"about_ca_system_score_gemma":0.00342818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415579,"about_ca_topic_score_gemma":0.0131376,"domain_scores_codex":[0.9991792,0.00007188667,0.0001004295,0.0003380277,0.0002510748,0.00005940202],"domain_scores_gemma":[0.9983711,0.0004137749,0.0001203658,0.0005485026,0.000413133,0.0001333064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001017302,0.0004255207,0.02519652,0.0003442402,0.000237265,0.001379605,0.001129816,0.1420272,0.03412347,0.02031679,0.05021442,0.7235879],"study_design_scores_gemma":[0.00006547535,0.0000481537,0.002632081,0.00002625945,0.00002652562,0.0001595411,0.0001225648,0.9442219,0.01498455,0.01499644,0.02266362,0.00005290596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01282831,0.00006537359,0.917355,0.0002011188,0.0001053447,0.0002274718,0.00384491,0.06323972,0.002132746],"genre_scores_gemma":[0.2198434,0.0002026659,0.7574141,0.0002060501,0.00007106172,0.0003633765,0.01474763,0.001901243,0.005250468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01415579,"threshold_uncertainty_score":0.0281468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1495463023783649,"score_gpt":0.2983499661939493,"score_spread":0.1488036638155844,"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."}}