{"id":"W2555304780","doi":"10.1016/j.jhydrol.2016.11.028","title":"Landscape-gradient assessment of thermokarst lake hydrology using water isotope tracers","year":2016,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Wilfrid Laurier University; Université Laval; Center for Northern Studies","funders":"","keywords":"Thermokarst; Permafrost; Subarctic climate; Environmental science; Tundra; Hydrology (agriculture); Peat; Meltwater; Arctic; Physical geography; Snow; Geology; Ecology; Oceanography; Geomorphology","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":[0.0005871506,0.0001262444,0.0003846739,0.0001858345,0.0000581692,0.000009014454,0.0002217856,0.0001177533,0.01258858],"category_scores_gemma":[0.0000122404,0.00006146538,0.0001365311,0.00006004343,0.0001355822,0.0001524638,0.00001602411,0.0001491358,0.00003242997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006808929,"about_ca_system_score_gemma":0.00004767282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001063545,"about_ca_topic_score_gemma":0.003346696,"domain_scores_codex":[0.998686,0.0001533496,0.0004871655,0.0001317262,0.0001859684,0.0003558561],"domain_scores_gemma":[0.9992229,0.0001622034,0.0003036726,0.0001337215,0.00006553344,0.0001119406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002142979,0.00005140931,0.9367433,0.0000138064,0.00009744422,0.0001557569,0.0005226488,0.001494108,0.05759612,0.0000145744,0.000428403,0.002668109],"study_design_scores_gemma":[0.00587644,0.005961601,0.9001608,0.0001292708,0.0003413041,0.005210401,0.0002705125,0.02434402,0.00474673,0.003120079,0.04919245,0.0006464207],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950153,0.0003229083,0.0001075735,0.001879602,0.000681092,0.00006068877,0.0001600515,0.000004110855,0.001768682],"genre_scores_gemma":[0.9989297,0.0002971327,0.000109981,0.0003362311,0.0002450291,1.982127e-7,0.00002786982,0.000004892726,0.00004898263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05284939,"threshold_uncertainty_score":0.988314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03217090953762264,"score_gpt":0.2663557825993281,"score_spread":0.2341848730617054,"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."}}