Species-specific mercury bioaccumulation in a diverse fish community
Bibliographic record
Abstract
Mercury bioaccumulation models developed for fish provide insight into the sources and transfer of Hg within ecosystems. Mercury concentrations were assessed for 16 fish species of the western reach of Lake Diefenbaker, Saskatchewan, Canada. For top predators (northern pike, Esox Lucius; walleye, Sander vitreum), Hg concentrations were positively correlated to δ(15)N, and δ(15)N to fish age, suggesting that throughout life these fish fed on organisms with increasingly higher trophic values and Hg concentrations. However, fish mass and/or age were the principal parameters related to Hg concentrations for most species. For 9 common species combined, individual variation in Hg concentration was explained in declining order of importance by fish mass, trophic position (δ(15)N), and fish age. Delta (15)N value was not the leading variable related to Hg concentration for the assemblage, probably because of the longevity of lower--trophic-level species (3 species ≥ 20 yr), substantial overlap in Hg concentration and δ(15)N values for large-bodied fish up to 3000 g, and complex relationships between Hg concentration and δ(15)N among species. These results suggest that the quantity of food (and Hg) consumed each year and converted to fish mass, the quantity of Hg bioaccumulated over years and decades, and trophic position were significant determinants of Hg concentration in Lake Diefenbaker fish.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".