Environmental and food web determinants of Lake Trout mercury concentrations in Ontario Lakes
Bibliographic record
Abstract
Prey composition and availability are considered a primary predictor of Lake Trout (Salvelinus namaycush) mercury (Hg) concentrations. Evidence from other freshwater fishes suggests that environmental and landscape factors likely also contribute to fish Hg dynamics, yet comprehensive, contemporary assessments for Lake Trout from boreal and north-temperate lakes are lacking. Here, we reassess the importance of prey characteristics using both previously published and contemporary data, incorporating additional variables and model complexity to better understand factors influencing Hg dynamics of Ontario Lake Trout. Our analyses indicate that 1) Lake Trout Hg concentrations are primarily associated with individual body size, 2) high dissolved organic carbon (DOC) concentrations elevate Hg for fish of a given size, and 3) a coarse categorization of food chain length, specifically the presence of Mysis diluviana, informs Hg biomagnification slopes. The inclusion of DOC was vital for assessing human consumption risk, as Lake Trout in high DOC lakes were more likely to exceed Hg guidelines at sizes often harvested by anglers. Drivers of Lake Trout Hg levels in boreal and north-temperate lakes closely match those reported to affect other fishes in the region, regardless of feeding, thermal, and habitat strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".