Temporal trends in a biomagnifying contaminant: Application of amino acid compound–specific stable nitrogen isotope analysis to the interpretation of bird mercury levels
Bibliographic record
Abstract
Temporal trends in levels of biomagnifying contaminants, such as mercury (Hg), in top predators can provide insights into changes in contaminant bioavailability through time. However, interpreting contaminant temporal trends in predators can be confounded by temporal changes in their diets, which, in turn, could affect organism trophic position and exposure to biomagnifying contaminants. To address this issue, bulk stable nitrogen isotope analysis, that is, analysis of whole tissue, is widely incorporated into contaminant-monitoring programs for the estimation of organism trophic position. In the present study, we investigated lake-specific temporal trends in Hg levels in herring gull (Larus argentatus smithsonianus) eggs from Lakes Huron and Erie, 2 Laurentian Great Lakes. Levels of Hg in Lake Huron eggs declined, whereas Lake Erie eggs showed no change. Stable nitrogen isotope analysis of bulk material could not explain these interlake differences in Hg temporal trends. However, application of amino acid compound-specific stable nitrogen isotope analysis, in conjunction with other dietary tracers (i.e., fatty acids), provided insights into the factors regulating interlake differences in Hg temporal trends. Adjusting Hg levels in predators to account for temporal changes in their diets can have a significant impact on the interpretation of temporal trend contaminants data. In this case, it reconciled the apparently different Hg temporal trends observed in gull eggs from Lakes Huron and Erie. Environ Toxicol Chem 2018;37:1458-1465. © 2018 SETAC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".