{"id":"W4393573007","doi":"10.5281/zenodo.3550915","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":"Figshare","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":"Training (meteorology); Streamflow; Statistics; Computer science; Geography; Mathematics; Meteorology; 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":[0.001220334,0.001300474,0.0005737246,0.0005516688,0.0004711852,0.0009482255,0.00144088,0.0009982641,0.01834645],"category_scores_gemma":[0.002809714,0.0003514942,0.001773317,0.0005209908,0.0002773815,0.001163068,0.0007855473,0.00152215,0.01090057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030225,"about_ca_system_score_gemma":0.0007662479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01987975,"about_ca_topic_score_gemma":0.03365189,"domain_scores_codex":[0.9995198,0.00007853703,0.00003539806,0.0001983341,0.0001137771,0.00005417425],"domain_scores_gemma":[0.9990989,0.0002949368,0.00005409352,0.0001953914,0.0002667388,0.00008990538],"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":[0.001032223,0.0002895592,0.03253071,0.0008183384,0.0002933199,0.0002400279,0.0001960135,0.03875538,0.007405446,0.001261271,0.8381297,0.07904797],"study_design_scores_gemma":[0.0008235353,0.001139259,0.1719953,0.0006322914,0.000449201,0.0004902829,0.0007665852,0.393598,0.04339735,0.008021749,0.3782808,0.0004056568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1508811,0.001276858,0.01865975,0.003375767,0.001816642,0.0003165342,0.7746303,0.03089175,0.01815127],"genre_scores_gemma":[0.1219544,0.0004172217,0.01666148,0.0007480037,0.0001364007,0.0003217567,0.8457382,0.001536489,0.01248587],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01987975,"threshold_uncertainty_score":0.06137502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1107133645260055,"score_gpt":0.2640823372140225,"score_spread":0.153368972688017,"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."}}