{"id":"W2008760455","doi":"10.1021/es035146n","title":"Snowmelt Sources of Methylmercury to High Arctic Ecosystems","year":2004,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wetland; Snowmelt; Arctic; Tributary; Methylmercury; Environmental science; Tundra; Ecosystem; Aquatic ecosystem; Drainage basin; Hydrology (agriculture); Ecology; Environmental chemistry; Oceanography; Geology; Surface runoff; Bioaccumulation; Geography; Chemistry; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004153215,0.0001900325,0.0002556198,0.0003421073,0.0003300234,0.0000160972,0.0007278575,0.00009134138,0.00149975],"category_scores_gemma":[0.00009069755,0.0001669853,0.00004938051,0.001450776,0.002669232,0.0003040139,0.0007441202,0.000137362,0.001385952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006432835,"about_ca_system_score_gemma":0.0000207506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006195222,"about_ca_topic_score_gemma":0.0001080289,"domain_scores_codex":[0.997981,0.00001819962,0.0003221822,0.0005211747,0.0006184354,0.0005390734],"domain_scores_gemma":[0.9991984,0.00002098446,0.0001375249,0.0004497749,0.000004143758,0.0001891568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008525868,0.0001749652,0.159403,0.000006169544,0.00001229428,0.000008013646,0.001202337,0.002097146,0.8262243,0.001714137,0.00009321624,0.009055872],"study_design_scores_gemma":[0.0005440229,0.000516934,0.1389434,0.00003576756,0.00002323939,0.00007060215,0.002885233,0.00001276187,0.839928,0.00684814,0.009769084,0.0004228054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960338,0.00009352429,0.0004862238,0.001362076,0.0001774085,0.0003086538,0.00001540257,0.00007122123,0.001451689],"genre_scores_gemma":[0.9970865,0.00003545659,0.002428132,0.0002128535,0.00001621765,0.00004020368,0.000001771346,0.00001178868,0.0001670756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02045951,"threshold_uncertainty_score":0.999413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007632595873472797,"score_gpt":0.2321054923115912,"score_spread":0.2244728964381184,"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."}}