{"id":"W2039454587","doi":"10.1021/es102840u","title":"Climate Change and Mercury Accumulation in Canadian High and Subarctic Lakes","year":2011,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Subarctic climate; Arctic; Mercury (programming language); Scavenging; Environmental science; Paleolimnology; Sediment; Deposition (geology); Thermokarst; Climate change; Physical geography; Environmental chemistry; Oceanography; Ecology; Geology; Chemistry; Geography; Biology; Geomorphology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002345906,0.0002445609,0.0002703662,0.001365298,0.001653058,0.0007805679,0.0004246281,0.0003039545,0.0006866493],"category_scores_gemma":[0.0007884248,0.0002271867,0.000238073,0.003216663,0.0006109879,0.000291274,0.0006178449,0.0002454743,0.00005814082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01581847,"about_ca_system_score_gemma":0.006778108,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9775066,"about_ca_topic_score_gemma":0.9911402,"domain_scores_codex":[0.9997852,0.00001342189,0.000009151155,0.00004759636,0.00006232227,0.00008221137],"domain_scores_gemma":[0.9993278,0.00004347997,0.0001754269,0.00001885809,0.0003058272,0.0001286114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008886353,0.00000915275,0.9945908,0.00001625572,0.00005033293,0.00005957919,0.0005392781,0.0001818775,0.001472779,0.00004169783,0.0002007998,0.002748574],"study_design_scores_gemma":[0.000001135173,0.000002893225,0.9995164,0.000001210385,0.000007346423,0.000009590976,0.000126127,0.00007460735,0.00006622092,0.000004405464,0.0001883244,0.000001696446],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986609,0.000306468,0.00001859825,0.00005151806,0.000001500035,0.000002676911,0.0004791099,0.000005579981,0.0004736166],"genre_scores_gemma":[0.9989327,0.0002262413,0.00009204703,0.00002575394,0.000002195786,0.000003273163,0.0004959401,0.000002526818,0.000219403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02249336,"threshold_uncertainty_score":0.1147715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03433617905812784,"score_gpt":0.2513146917029432,"score_spread":0.2169785126448154,"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."}}