{"id":"W3014107227","doi":"","title":"Machine Learning for Streamflow Prediction: Current Status and Future Prospects","year":2019,"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":"University of Waterloo","funders":"","keywords":"Streamflow; Current (fluid); Computer science; Machine learning; Environmental science; Artificial intelligence; Geography; Engineering; Cartography; Drainage basin","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":[0.00584057,0.0006767062,0.001312509,0.0008964463,0.0002652627,0.002577221,0.001255135,0.001599783,0.004844674],"category_scores_gemma":[0.005353324,0.0002073427,0.0006044264,0.002054141,0.001214329,0.003858027,0.000933803,0.002026106,0.001590727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007475526,"about_ca_system_score_gemma":0.001106343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002974055,"about_ca_topic_score_gemma":0.002371295,"domain_scores_codex":[0.9991424,0.0003281593,0.00004932598,0.0001707386,0.0002336101,0.00007584535],"domain_scores_gemma":[0.9931758,0.005031838,0.000236758,0.0002027681,0.00113277,0.0002200462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002893493,0.0002797343,0.003946623,0.001090762,0.0001118833,0.00002073941,0.00004819638,0.008565014,0.0009842714,0.0087863,0.01544133,0.9604359],"study_design_scores_gemma":[0.0002485363,0.001634274,0.01591834,0.004892416,0.0004635973,0.0002406536,0.0007173255,0.4432064,0.008323761,0.1520714,0.3720506,0.000232701],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.02121145,0.8347355,0.09175204,0.03447869,0.002245472,0.00006550044,0.000382015,0.0007164161,0.01441305],"genre_scores_gemma":[0.2951118,0.5787053,0.1002495,0.004328384,0.007612771,0.0001566683,0.001240609,0.0001231813,0.01247188],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00584057,"threshold_uncertainty_score":0.03088826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184844199857864,"score_gpt":0.2307383291524525,"score_spread":0.2188898871538739,"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."}}