Lake depth influences mercury and omega-3 levels in Walleye via resource utilization shifts
Bibliographic record
Abstract
Elevated mercury levels in fish are correlated with their body size and trophic position, and with environmental parameters (e.g., catchment and lake properties). Much less is known how the variation of polyunsaturated fatty acids (PUFA) in fish is intertwined with environmental variables and mercury levels. We studied the linkages between catchment and lake properties and the variation of eicosapentaenoic acid (EPA), docosahexanenoic acid (DHA) and mercury levels in Walleye (Percidae, Sander vitreus) from 30 lakes in the Province of Ontario, Canada. Walleye mercury and DHA levels correlated with fish length; thus, we used length-standardized mass fractions in the correlation analyses of lake and catchment properties and the intraspecific variation of mercury, EPA and DHA in Walleye. Overall, the data indicated that mercury, EPA and DHA levels in Walleye are linked to habitat availability, i.e., relative abundance of pelagic vs. littoral areas, and consequently, to differences of the reliance on pelagic vs. littoral or benthic food webs. The length-standardized mass fractions of mercury, EPA, and DHA increased with increasing maximum depth of a lake, which explained 35% of the total variation. Habitat availability may be integral in determining the foraging grounds and diet selection of Walleye, which in turn is linked with muscle EPA, DHA, and mercury levels, as well as the risk and benefits of consuming Walleye for humans. Thus, the findings have direct applicability to informing lake-specific consumption advisories for Walleye.
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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 | 0.000 | 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) | 0.001 | 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".