Mercury accumulation, biomagnification, and relationships to δ13C, δ15N and δ34S of fishes and marine mammals in a coastal Arctic marine food web
Bibliographic record
Abstract
Combining mercury and stable isotope data sets of consumers facilitates the quantification of whether contaminant variation in predators is due to diet, habitat use and/or environmental factors. We investigated inter-species variation in total Hg (THg) concentrations, trophic magnification slope between δ15N and THg, and relationships of THg with δ13C and δ34S in 15 fish and four marine mammal species (249 individuals in total) in coastal Arctic waters. Median THg concentration in muscle varied between species ranging from 0.08 ± 0.04 μg g−1 dw in capelin to 3.10 ± 0.80 μg g−1 dw in beluga whales. Both δ15N (r2 = 0.26) and δ34S (r2 = 0.19) best explained variation in log-THg across consumers. Higher THg concentrations occurred in higher trophic level species that consumed more pelagic-associated prey than consumers that rely on the benthic microbial-based food web. Our study illustrates the importance of using a multi-isotopic approach that includes δ34S when investigating trophic Hg dynamics in coastal marine systems.
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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".