Spatial 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
Abstract Levels of biomagnifying contaminants are greatest in high–trophic level biota (e.g., predatory birds such as gulls). Gull eggs have been used to assess contaminant spatial patterns and sources, but such assessments must consider how organism trophic position may influence spatial inferences. Stable nitrogen isotopes (δ15N) in bulk tissue are routinely used in this context. However, bulk δ15N values are only useful if spatial differences in baseline δ15N values are considered. Amino acid compound–specific stable nitrogen isotope analysis can generate estimates of baseline δ15N values and trophic position from the same sample. In the present study, eggs (n = 428) of California (Larus californicus), herring (Larus argentatus smithsonianus), and ring-billed (Larus delawarensis) gulls were used to assess spatial patterns in mercury (Hg) availability in 12 western Canadian lakes located over 14 degrees of latitude, with amino acid compound–specific stable isotope analysis adjustment of egg Hg levels for trophic position. Mean trophic position–adjusted egg Hg levels (micrograms per gram, dry wt) were greatest at sites in receiving waters of the Athabasca River (X¯ = 0.70) compared to southern (X¯ = 0.39) and northern (X¯ = 0.50) regions. Research is required to investigate factors (e.g., local Hg released as a result of human activities, processes influencing Hg methylation) which may be responsible for greater Hg availability in the lower Athabasca River basin. However, it is clear that amino acid compound–specific stable isotope analysis is a valuable tool for assessing contaminant spatial patterns. Environ Toxicol Chem 2018;37:1466–1475. © 2018 SETAC Abstract
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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 | 0.001 |
| 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".