{"id":"W2083074703","doi":"10.1021/es801619h","title":"Metamorphosis in Chironomids, More than Mercury Supply, Controls Methylmercury Transfer to Fish in High Arctic Lakes","year":2008,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Methylmercury; Mercury (programming language); Arctic; Environmental science; Environmental chemistry; Salvelinus; Sediment; Food web; Arctic char; Aquatic ecosystem; Ecology; Trout; Chemistry; Bioaccumulation; Fishery; Predation; Biology; Fish <Actinopterygii>","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114629,0.000162937,0.0001458076,0.0002731552,0.000504923,0.0003052992,0.0001573641,0.0001376863,0.0003388425],"category_scores_gemma":[0.000287378,0.0001870374,0.0001016259,0.0002030156,0.0003197854,0.0001407616,0.0002042845,0.00009760402,0.00007459427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046167,"about_ca_system_score_gemma":0.0006430838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1201777,"about_ca_topic_score_gemma":0.2983775,"domain_scores_codex":[0.9999201,0.00001425616,0.000003477534,0.00001949081,0.00001738232,0.0000252534],"domain_scores_gemma":[0.9998299,0.00002178685,0.0000660646,0.000007686601,0.00004071943,0.00003379586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003269803,0.00003765763,0.8720387,0.00002825215,0.00005129688,0.0000943296,0.0006067012,0.0003810702,0.1204859,0.00004482239,0.0000915388,0.005812751],"study_design_scores_gemma":[0.000001311363,0.00002826899,0.9988139,9.178015e-7,0.000006435871,0.00001149598,0.0001127438,0.0001608569,0.0008112933,0.000007132384,0.00004466707,0.00000103044],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998441,0.000029596,0.00001792477,0.000002621804,1.817622e-7,6.507901e-7,0.0000138217,0.000001253364,0.00008989224],"genre_scores_gemma":[0.9996322,0.00004696678,0.00007128898,0.000005796081,6.835803e-7,0.000002336401,0.00005073619,0.000001047506,0.0001888238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1201777,"threshold_uncertainty_score":0.2389563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009631025147693114,"score_gpt":0.2269326196298109,"score_spread":0.2173015944821178,"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."}}