{"id":"W3210676385","doi":"10.5281/zenodo.3648270","title":"Models and Predictions for \"The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction\"","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Streamflow; Training (meteorology); Environmental science; Hydrology (agriculture); Econometrics; Computer science; Geography; Mathematics; Meteorology; Geology; Cartography; Geotechnical engineering","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":[0.001285654,0.001397053,0.0005930027,0.0005287122,0.0004750365,0.0009649166,0.001500871,0.001079823,0.01582237],"category_scores_gemma":[0.003015817,0.0003864526,0.001840235,0.0004812651,0.0002976124,0.001150743,0.0008239732,0.001671887,0.009399296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059667,"about_ca_system_score_gemma":0.0008230614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02066385,"about_ca_topic_score_gemma":0.0341452,"domain_scores_codex":[0.9995077,0.00008307984,0.00003523126,0.0002059305,0.0001105507,0.00005749857],"domain_scores_gemma":[0.9990388,0.0003321587,0.00005638688,0.0001961494,0.0002878542,0.00008874861],"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":[0.001236116,0.000348305,0.03719776,0.0009220253,0.0003883847,0.0002802846,0.0002129167,0.06999215,0.00970629,0.001548728,0.7833121,0.09485496],"study_design_scores_gemma":[0.0007773442,0.001043835,0.1427674,0.0005595485,0.0004265842,0.0004066227,0.0006523581,0.5399398,0.0425152,0.008117643,0.2624082,0.0003854286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1918312,0.001565014,0.02548094,0.004061095,0.002149252,0.0003643084,0.7253525,0.03158616,0.01760953],"genre_scores_gemma":[0.1618497,0.0004900303,0.02086737,0.0008286337,0.0001630106,0.0003641419,0.8009396,0.001508758,0.01298871],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02066385,"threshold_uncertainty_score":0.05293113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0817195043559592,"score_gpt":0.2487375490217122,"score_spread":0.167018044665753,"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."}}