{"id":"W2085267618","doi":"10.1016/j.scitotenv.2014.07.125","title":"Defining fish community structure in Lake Winnipeg using stable isotopes (δ13C, δ15N, δ34S): Implications for monitoring ecological responses and trophodynamics of mercury &amp; other trace elements","year":2014,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; University of Saskatchewan","funders":"Environment Canada; U.S. Environmental Protection Agency","keywords":"Trophic level; Biomagnification; Environmental science; Mercury (programming language); Ecology; Bioaccumulation; Eutrophication; δ15N; Trace element; Trophic state index; Food web; Environmental chemistry; δ13C; Stable isotope ratio; Biology; Nutrient; Chemistry","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":[0.0002950924,0.0002664771,0.0002178466,0.001455134,0.002110319,0.0008523494,0.0006085528,0.000261414,0.001144347],"category_scores_gemma":[0.0006017826,0.0003333862,0.0002222285,0.00118202,0.0007669159,0.0004557087,0.001087345,0.0001894107,0.0001198631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007970161,"about_ca_system_score_gemma":0.004309615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9129204,"about_ca_topic_score_gemma":0.9732496,"domain_scores_codex":[0.9998108,0.00001870706,0.000009169209,0.00004510197,0.00003812795,0.0000781346],"domain_scores_gemma":[0.9997995,0.00001092359,0.00004015254,0.000004161831,0.00007753226,0.0000676771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003946057,0.00003525164,0.9578775,0.0001101687,0.0001325222,0.000283268,0.005313515,0.0003597076,0.01910957,0.0003660468,0.0004321555,0.01558573],"study_design_scores_gemma":[0.000006811439,0.00002753854,0.9958437,0.00001086787,0.00002325229,0.00005021628,0.00226832,0.0004613263,0.0005156209,0.00004556944,0.0007398484,0.000006967304],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989938,0.000115987,0.0001431224,0.00005538168,0.000002352076,0.0000247911,0.0002100377,0.000004081543,0.000450359],"genre_scores_gemma":[0.9981905,0.00009371327,0.0007020604,0.00003485463,0.000001047927,0.00001707956,0.0001090471,0.000003130596,0.0008486976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08707964,"threshold_uncertainty_score":0.1751849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03217692328571659,"score_gpt":0.2885430066351214,"score_spread":0.2563660833494049,"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."}}