{"id":"W7113707764","doi":"","title":"Terrestrial carbon inputs drive methylmercury accumulation in zooplankton of boreal and subarctic lakes","year":2025,"lang":"en","type":"article","venue":"Epsilon Open Archive (Sveriges lantbruksuniversitet biblioteket (Swedish University of Agricultural Sciences))","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Norsk Institutt for Vannforskning; Norges Forskningsråd","keywords":"Zooplankton; Subarctic climate; Bioaccumulation; Dissolved organic carbon; Methylmercury; Food web; Hypolimnion; Biomagnification","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001081792,0.0002903681,0.0001931675,0.0005508799,0.0004517021,0.0004675486,0.0001797863,0.0002287694,0.0004530261],"category_scores_gemma":[0.000272567,0.0002747775,0.0002281351,0.0003793719,0.0002761387,0.0003262336,0.0003949567,0.0001478763,0.0001031749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005433223,"about_ca_system_score_gemma":0.0003538476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03273017,"about_ca_topic_score_gemma":0.05459499,"domain_scores_codex":[0.999927,0.000008396199,0.000007379213,0.00002752436,0.00001455715,0.00001515225],"domain_scores_gemma":[0.9997446,0.00002320474,0.0001178612,0.00000811611,0.000051985,0.00005430728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001619938,0.00002276797,0.9459852,0.00002255034,0.00005275568,0.00007969062,0.0003196113,0.0001392308,0.05070126,0.00001554568,0.00004904206,0.00245033],"study_design_scores_gemma":[0.000001260804,0.00001910214,0.9994491,9.825181e-7,0.000006228347,0.00001015649,0.00009315772,0.0001019542,0.0002815315,0.000003958367,0.00003134414,0.000001248527],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997153,0.00005722787,0.00001752092,0.000005188207,8.241064e-7,0.000001186101,0.00006196731,0.000002195587,0.0001385942],"genre_scores_gemma":[0.999504,0.00006685139,0.00009966406,0.0000150305,0.000002408026,0.000003651893,0.0001707333,0.000001568141,0.0001362387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03273017,"threshold_uncertainty_score":0.06507933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257521346042936,"score_gpt":0.2747915924884332,"score_spread":0.2522163790280039,"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."}}