{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003007016,0.0001152724,0.0001089279,0.0003025983,0.0002780035,0.00001449456,0.0001596631,0.00007737223,0.0006972718],"category_scores_gemma":[0.00002460907,0.0001078311,0.000007521967,0.0004771995,0.00220879,0.0005311229,0.0002954649,0.00009927692,0.0001185084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002581915,"about_ca_system_score_gemma":0.000007325431,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03962702,"about_ca_topic_score_gemma":0.06569401,"domain_scores_codex":[0.9988326,0.00001365728,0.0001288734,0.000351161,0.0001545255,0.0005191389],"domain_scores_gemma":[0.9996162,0.000009165124,0.00004201851,0.0001486154,9.15824e-7,0.0001830846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002368584,0.00001620982,0.9648923,0.000001351988,8.03629e-7,0.000006273171,0.001261057,7.654494e-7,0.01104485,0.0008225263,0.000004040504,0.02194745],"study_design_scores_gemma":[0.0001385516,0.00006081301,0.9933851,0.000006013227,0.000004344622,0.00002143198,0.0007330265,0.0001017251,0.003339212,0.001469753,0.0006084245,0.0001316207],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969977,0.0001383667,0.000002625118,0.0006009658,0.00004865139,0.0002085656,0.000009346423,0.00002504895,0.001968729],"genre_scores_gemma":[0.9990082,0.0004248763,0.0003151573,0.0001922347,0.000005849014,0.00002640257,0.000001812011,0.000005837986,0.00001964562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02849278,"threshold_uncertainty_score":0.9667682,"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."}}