{"id":"W3210625331","doi":"10.5281/zenodo.4095485","title":"Models and Predictions for \"Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network\"","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Term (time); Surface runoff; Environmental science; Meteorology; Computer science; Geography; Physics; Ecology","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":[0.001270756,0.0013288,0.0006338324,0.0007467339,0.0004761685,0.001479886,0.003001121,0.001650244,0.1091695],"category_scores_gemma":[0.004130836,0.0007914866,0.002140152,0.001431231,0.0002103182,0.002862806,0.001481392,0.001625726,0.05950263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138093,"about_ca_system_score_gemma":0.001693175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01981661,"about_ca_topic_score_gemma":0.0168343,"domain_scores_codex":[0.9994005,0.00009412407,0.00004519608,0.0001269662,0.0002768574,0.00005632537],"domain_scores_gemma":[0.9989195,0.0002853099,0.00005701526,0.0002432271,0.0004169937,0.00007799501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001396542,0.00007061628,0.001647151,0.0004147472,0.0001476878,0.00006093247,0.0000411828,0.04010796,0.001097139,0.007078248,0.9130743,0.03612036],"study_design_scores_gemma":[0.0004808624,0.00008677528,0.004634599,0.0002516543,0.0001539566,0.00008862807,0.00007018356,0.2633241,0.00983996,0.02469553,0.6962452,0.0001285539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008628219,0.0009373434,0.1211943,0.005531643,0.002056982,0.000466897,0.7008799,0.08479429,0.07551045],"genre_scores_gemma":[0.04846598,0.001184886,0.0861923,0.001467089,0.0005240611,0.001027357,0.7700891,0.01968513,0.0713641],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1091695,"threshold_uncertainty_score":0.3652084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04872614810696903,"score_gpt":0.2259776562196659,"score_spread":0.1772515081126969,"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."}}