{"id":"W4393484683","doi":"10.5281/zenodo.10223596","title":"Generative deep learning for hydrological forecasting: CVAE-75 basins from CANOPEX_v1","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Generative grammar; Artificial intelligence; Climatology; Computer science; Econometrics; Environmental science; Geography; Machine learning; Meteorology; Geology; Mathematics","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":[0.0007989279,0.00256418,0.0008865547,0.001352634,0.0005638908,0.001214308,0.003245953,0.002077079,0.03790941],"category_scores_gemma":[0.002611928,0.0008800702,0.001674822,0.001928895,0.0005450602,0.0008707992,0.001478586,0.002392034,0.03004873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001180501,"about_ca_system_score_gemma":0.001700123,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04295306,"about_ca_topic_score_gemma":0.07910916,"domain_scores_codex":[0.9996397,0.00006504964,0.00002032612,0.0001075361,0.00009924194,0.00006803434],"domain_scores_gemma":[0.9994084,0.0001506595,0.00002454629,0.0002223556,0.0001244295,0.00006976048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001343056,0.0001457123,0.00173267,0.0002978129,0.0001052403,0.00007144954,0.00003244825,0.02635438,0.0004847118,0.001103838,0.9579498,0.0115877],"study_design_scores_gemma":[0.001439151,0.0001975777,0.01174282,0.0003794166,0.0001085143,0.0002110481,0.0001223936,0.239042,0.005604781,0.01593077,0.7250257,0.0001959468],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0098183,0.0004006635,0.003309392,0.0005531483,0.0002340732,0.0001085254,0.9585447,0.02170413,0.005327076],"genre_scores_gemma":[0.01155428,0.0001100272,0.004504862,0.0001251121,0.00002124069,0.0001317104,0.9805792,0.0008280866,0.002145575],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9570469,"threshold_uncertainty_score":0.1268196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06913441974173826,"score_gpt":0.2564358105716017,"score_spread":0.1873013908298634,"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."}}