{"id":"W1993170471","doi":"10.5194/hess-19-2469-2015","title":"Shallow groundwater thermal sensitivity to climate change and land cover disturbances: derivation of analytical expressions and implications for stream temperature modeling","year":2015,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Fisheries and Oceans Canada; University of Calgary; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey","keywords":"Groundwater; Environmental science; Climate change; Hydrology (agriculture); Advection; Aquifer; Global warming; Surface water; Land cover; STREAMS; Thermal; Geology; Land use; Ecology; Meteorology; Geography; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006651449,0.00008350472,0.0001539471,0.00003450986,0.0003642519,0.00002183753,0.00004475123,0.00005858547,0.000002615674],"category_scores_gemma":[0.00002081991,0.00005680491,0.00001162103,0.00007716946,0.000381849,0.0002174502,0.0001491466,0.00003428997,0.000002511529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006136172,"about_ca_system_score_gemma":0.000002944464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001139435,"about_ca_topic_score_gemma":0.0002376975,"domain_scores_codex":[0.9992707,0.00006287453,0.0001172008,0.0002772277,0.00007897178,0.0001930231],"domain_scores_gemma":[0.9997429,0.00006177642,0.00003712655,0.00007050653,0.00001231537,0.00007533775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006686378,0.00002847459,0.9824339,0.00005521478,0.0000177401,0.000001599265,0.001928131,0.01187243,0.001180666,0.001776296,0.00004906645,0.0005895981],"study_design_scores_gemma":[0.0007336996,0.0006284124,0.6097716,0.00007485806,0.00008098431,0.00004363155,0.0009267713,0.3862773,0.0001148867,0.0007976973,0.0002811764,0.0002689356],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966035,0.0001038898,0.0006731761,0.001658593,0.00003902042,0.0002715992,0.00001411972,0.00001313256,0.0006229953],"genre_scores_gemma":[0.9994284,0.0000290824,0.0002616601,0.0001947804,0.00002275887,0.00004150036,0.000002795038,0.000002232933,0.00001680157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3744049,"threshold_uncertainty_score":0.2801568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04858192390257261,"score_gpt":0.2627042577211241,"score_spread":0.2141223338185515,"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."}}