Exploring the spatial variation of mercury in the Gulf of St. Lawrence using northern gannets as fish samplers
Bibliographic record
Abstract
Mercury (Hg) is a ubiquitous and pervasive environmental contaminant with detrimental effects on wildlife, which originates from both natural and anthropogenic sources. Its distribution within ecosystems is influenced by various biogeochemical processes, making it crucial to elucidate the factors driving this variability. To explore these factors, we employed an innovative method to use northern gannets (Morus bassanus) as biological samplers of regurgitated fish in the Gulf of St. Lawrence. We assessed fish total Hg (THg) concentrations in relation to their geographical catch location as well as to pertinent biotic and anthropogenic factors. In small fish species, trophic position, calculated from compound-specific stable nitrogen isotopes in amino acids, emerged as the most influential predictor of THg concentrations. For large fish species, THg concentrations were best explained by δ13C, indicating higher concentrations in inshore habitats. No anthropogenic factors, such as pollution, shipping traffic, or coastal development, were significantly related to THg concentrations in fish. Moreover, previously published THg data in mussels sampled nearby were positively linked with THg concentrations in gannet prey, suggesting consistent mercury distribution across trophic levels in the Gulf of St. Lawrence. Our findings point to habitat-dependent variability in THg concentrations across multiple trophic levels. Our study could have many potential uses in the future, including the identification of vulnerability hotspots for fish populations and their predators, or assessing risk factors for seabirds themselves by using biologically relevant prey.
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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".