{"id":"W2955990221","doi":"10.1016/j.scitotenv.2019.06.427","title":"Using stable isotopes paired with tritium analysis to assess thermokarst lake water balances in the Source Area of the Yellow River, northeastern Qinghai-Tibet Plateau, China","year":2019,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Environment and Protected Areas; University of Victoria","funders":"Fundamental Research Funds for the Central Universities; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Thermokarst; Permafrost; Wetland; Environmental science; Hydrology (agriculture); Precipitation; Groundwater; Water balance; Plateau (mathematics); Geology; Ecology; Geography; Oceanography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001207515,0.0001569919,0.000233133,0.00006662575,0.0003343352,0.00006579564,0.001298941,0.00002693157,0.001023742],"category_scores_gemma":[0.000008206953,0.00005288211,0.0001143113,0.0006388649,0.0008294219,0.0001908789,0.0001678395,0.0001314543,0.00002539377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001374439,"about_ca_system_score_gemma":0.0000259467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003998473,"about_ca_topic_score_gemma":0.00452472,"domain_scores_codex":[0.9981237,0.0001738031,0.0002241578,0.0002760228,0.0008146625,0.0003876548],"domain_scores_gemma":[0.9989941,0.0001040091,0.0001417896,0.0007054738,0.000009566989,0.00004509952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005267382,0.00003818591,0.3707899,0.0000105806,0.00004025671,6.869876e-7,0.007345722,0.6056549,0.0158332,0.000001788479,0.000003883292,0.0002282533],"study_design_scores_gemma":[0.0002107827,0.0001005565,0.9341791,0.00005138361,0.0001106332,0.000007278824,0.002162434,0.05646015,0.006440732,0.00003623825,0.0001020068,0.0001387292],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973748,0.0000551694,0.00001575888,0.001007368,0.0001094238,0.0004721348,0.0002253594,0.000003238782,0.0007367929],"genre_scores_gemma":[0.9993433,0.00001620919,0.00002052705,0.00007513769,0.00001917186,0.000002207358,0.00001224304,0.000003672324,0.0005075714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5633892,"threshold_uncertainty_score":0.9998894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0305209029968019,"score_gpt":0.2069983637235597,"score_spread":0.1764774607267578,"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."}}