{"id":"W4393685077","doi":"10.5281/zenodo.3543549","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; Computer science; Statistics; Meteorology; Geography; Mathematics; Cartography","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":[0.001205854,0.001283234,0.0005611744,0.0005243691,0.000481793,0.0009143762,0.001384286,0.001018354,0.01699555],"category_scores_gemma":[0.002793866,0.0003463301,0.001728851,0.0004729808,0.0002900723,0.001141613,0.0007979365,0.00151547,0.009856829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009762946,"about_ca_system_score_gemma":0.0007596688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01813935,"about_ca_topic_score_gemma":0.03101499,"domain_scores_codex":[0.9995244,0.00007753377,0.00003460835,0.0001965864,0.0001129707,0.00005402075],"domain_scores_gemma":[0.999074,0.0002964025,0.00005611735,0.0002030439,0.0002765509,0.0000938781],"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":[0.00126099,0.000345556,0.03906553,0.0008907966,0.000337917,0.0002861494,0.0002431621,0.04418929,0.009892881,0.001368514,0.8067819,0.09533739],"study_design_scores_gemma":[0.0008265254,0.001324547,0.1912478,0.0006017684,0.0004726304,0.0005005587,0.0008088835,0.4154504,0.04964554,0.007810642,0.3308855,0.0004251867],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2054801,0.001431369,0.02180737,0.003741962,0.002172766,0.0003781232,0.7120476,0.03279081,0.02014987],"genre_scores_gemma":[0.1545392,0.0004205457,0.01825686,0.0008053479,0.0001571283,0.000355201,0.8096239,0.00152653,0.01431539],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01813935,"threshold_uncertainty_score":0.0568558,"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."}}