{"id":"W4405893035","doi":"10.22541/essoar.173557434.40176318/v1","title":"Increased Streamflow Intermittence in Europe due to Climate Change Projected by Combining Global Hydrological Modeling and Machine Learning","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Streamflow; Climatology; Climate change; Environmental science; Climate model; Meteorology; Geography; Geology; Oceanography; Cartography; Drainage basin","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":[0.001261699,0.0004765182,0.0003234445,0.0008338523,0.0001977197,0.0007994096,0.000269062,0.0005569071,0.0004391494],"category_scores_gemma":[0.001291574,0.0002031529,0.0007360497,0.001387796,0.0002589518,0.0007244715,0.0004678466,0.0003238129,0.00007448027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006270043,"about_ca_system_score_gemma":0.0004000674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0173401,"about_ca_topic_score_gemma":0.01135921,"domain_scores_codex":[0.9997823,0.0000517364,0.00001963917,0.00008140039,0.0000331341,0.00003180795],"domain_scores_gemma":[0.9996219,0.0001090629,0.00009024854,0.00007151977,0.00006046555,0.0000469115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001164914,0.00009225481,0.3602966,0.00007018082,0.0003910901,0.0002201692,0.00006294365,0.6053278,0.002600436,0.00119571,0.0009334348,0.02869279],"study_design_scores_gemma":[0.00002979405,0.00006117354,0.4165993,0.00003719518,0.00009535213,0.00006422626,0.00004871332,0.5782568,0.001632266,0.001226098,0.001912523,0.00003660072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940484,0.0001866279,0.003737557,0.00009726309,0.00001290583,0.000006044321,0.001144858,0.0001936884,0.0005727457],"genre_scores_gemma":[0.9962074,0.0001352105,0.002060164,0.00002047876,0.000008752902,0.000007081444,0.001451876,0.00001762952,0.00009150101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0173401,"threshold_uncertainty_score":0.03447831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0264585516831134,"score_gpt":0.2505030588911762,"score_spread":0.2240445072080628,"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."}}