Temporal changes in mercury bioaccumulation by predatory fishes of boreal lakes following the invasion of an exotic forage fish
Bibliographic record
Abstract
We evaluated the prediction that mercury concentrations of predatory fishes in boreal lakes would rise following the invasion of an exotic forage fish species (rainbow smelt, Osmerus mordax) that was believed to feed at a higher trophic position than native forage fishes. We compared temporal trends (postinvasion minus preinvasion values) in fish mercury bioaccumulation between lakes experiencing recent smelt invasions and reference lakes of central Canada. Piscivore mercury concentrations in this region have remained stable or declined during approximately the last 20 years. These trends were not strongly influenced by the smelt invasion, despite the fact that smelt were a major prey item for all piscivore species examined. The effect of smelt invasion on mercury bioaccumulation in the predator species reflected the importance of smelt in their respective diets (lake trout, Salvelinus namaycush > walleye, Stizostedion vitreum > northern pike, Esox lucius). However, these effects were not statistically significant for any piscivore species. The impact of rainbow smelt invasion on mercury bioaccumulation in native piscivores of this region has been much less than previous food-web studies have predicted.
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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) | 0.000 | 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".