Cross-basin comparison of mercury bioaccumulation in Lake Huron lake trout emphasizes ecological characteristics
Bibliographic record
Abstract
Understanding factors influencing mercury (Hg) bioaccumulation in fish is important for examining both ecosystem and human health. However, little is known about how differing ecosystem and biological characteristics can drive Hg bioaccumulation in top predators. The present study compared and contrasted Hg bioaccumulation in multiple age classes of lake trout (Salvelinus namaycush) collected from each of Lake Huron's Georgian Bay, North Channel, and Main Basin regions. Mercury concentrations exhibited a basin specific pattern with Main Basin fish having the highest average concentration (0.19 ± 0.01 mg/kg), followed by Georgian Bay (0.15 ± 0.02 mg/kg), and North Channel (0.07 ± <0.01 mg/kg) fish. Age-related increases in Hg concentrations were observed across the 3 basins with North Channel fish exhibiting the slowest rate of Hg bioaccumulation. No significant difference was determined between the relationships describing Hg concentration and age between Main Basin and Georgian Bay fish (p < 0.05). Mercury biomagnification factors (BMF) determined between lake trout and rainbow smelt, lake trout's primary prey, were significantly correlated with fish age and differed across the 3 basins (p < 0.05). Specifically, Georgian Bay fish exhibited the greatest age related increase in Hg BMF followed by Main Basin and North Channel fish, and these differences could not be attributed to trophic level (δ(15)N) effects or lake trout growth rates. A highly significant negative relationship was determined between Hg BMFs and basin specific prey fish densities indicating that ecological factors associated with food acquisition and foraging efficiencies play an important role in Hg bioaccumulation in feral fish communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".