{"id":"W6927320567","doi":"10.3389/frwa.2022.934709.s001","title":"Data_Sheet_1_Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada.pdf","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Enterobacteriaceae and Cronobacter Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Streamflow; Flood forecasting; Precipitation; Interpretability; Pluvial; Sensitivity (control systems); Climate change; Hydrological modelling","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001177248,0.0003366189,0.0003168957,0.00005550539,0.0002452627,0.0001845821,0.0005642032,0.0002440323,0.05873453],"category_scores_gemma":[0.0005497409,0.0003678984,0.0000815472,0.00005850132,0.00001158804,0.00001221996,0.001291015,0.0005576307,0.00001486479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009279456,"about_ca_system_score_gemma":0.0003589719,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04824106,"about_ca_topic_score_gemma":0.5790008,"domain_scores_codex":[0.9980753,0.0001081633,0.0003212938,0.0007110595,0.0002190271,0.0005651644],"domain_scores_gemma":[0.9991891,0.00004380531,0.0001483918,0.0004411444,0.00006278569,0.0001148207],"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":[0.0002193919,0.00002617918,0.000225337,0.0004964176,0.00006549957,0.00003183412,0.00004459059,0.0005478196,0.0002562119,1.77599e-8,0.9976212,0.0004654978],"study_design_scores_gemma":[0.0005920469,0.0001754904,0.00002003727,0.000442797,0.00001325961,0.00001679928,0.0003349016,0.002676805,0.00016559,5.304042e-7,0.9951707,0.0003911177],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.015729,0.0006447554,0.00000465008,0.00001775775,0.00009366204,0.0003736316,0.9831143,0.000007044126,0.00001521255],"genre_scores_gemma":[0.006373931,0.0001180858,0.00001352552,0.0001051904,0.0002621599,0.0003464091,0.9925283,0.00004838505,0.0002040121],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5307598,"threshold_uncertainty_score":0.9998773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02146540660353964,"score_gpt":0.291281809302793,"score_spread":0.2698164026992533,"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."}}