{"id":"W2905758825","doi":"10.1175/jhm-d-18-0187.1","title":"Impact of Future Climate and Vegetation on the Hydrology of an Arctic Headwater Basin at the Tundra–Taiga Transition","year":2018,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Consejo Nacional de Innovación, Ciencia y Tecnología; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Global Water Futures; University of Saskatchewan","keywords":"Environmental science; Tundra; Evapotranspiration; Permafrost; Arctic; Climate change; Snowmelt; Snow; Surface runoff; Streamflow; Global warming; Precipitation; Hydrology (agriculture); Climatology; Taiga; Drainage basin; Geology; Ecology; Oceanography; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002561522,0.0002492458,0.0002008559,0.0002823117,0.0007401878,0.0009713679,0.0004151723,0.000453686,0.001011312],"category_scores_gemma":[0.0004637849,0.0001611357,0.0005731616,0.0003558221,0.0003726737,0.0003165579,0.0003487084,0.0002916066,0.00006396081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004355871,"about_ca_system_score_gemma":0.002575446,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5526767,"about_ca_topic_score_gemma":0.5807828,"domain_scores_codex":[0.9998988,0.00002683666,0.000004720991,0.00001675917,0.00001555075,0.00003737788],"domain_scores_gemma":[0.9998744,0.00002745124,0.00001605715,0.000008674866,0.00002734868,0.00004615219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003432123,0.0002501256,0.6085303,0.0000485318,0.0002388507,0.0006955822,0.0002672043,0.3731812,0.009465666,0.0009678672,0.0008764201,0.005135086],"study_design_scores_gemma":[0.00008458717,0.0001075878,0.5268085,0.00001798757,0.00009320228,0.00009332653,0.001016873,0.4686698,0.00158418,0.0003877419,0.001100741,0.00003545016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993005,0.00001842091,0.00007945226,0.0000674554,0.00000301667,0.000002643811,0.0001674105,0.00001087066,0.0003503909],"genre_scores_gemma":[0.9996062,0.00002009078,0.0001068534,0.00001207586,0.000001441639,0.000003020366,0.0001418046,0.00000211133,0.0001063696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5526767,"threshold_uncertainty_score":0.8999153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01887355572199453,"score_gpt":0.2574911964759776,"score_spread":0.238617640753983,"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."}}