Using Sulfur Stable Isotopes to Understand Feeding Behavior and Selenium Concentrations in Yellow Perch (<i>Perca flavescens</i>)
Bibliographic record
Abstract
We measured selenium (Se) concentrations in yellow perch (Perca flavescens) muscle and their prey collected from four Se-contaminated lakes located near metal smelters in the eastern Canadian cities of Sudbury and Rouyn-Noranda. Yellow perch Se concentrations were related to their weight in two of the four lakes. Measurements of sulfur stable isotopes (δ(34)S) in yellow perch muscle and stomach contents showed that larger fish tended to feed less on zooplankton and more on benthic invertebrates than did smaller fish. Because Se concentrations are lower and δ(34)S signatures are higher in zooplankton than in sediment-feeding invertebrates, there was an inverse relationship between animal Se concentrations and δ(34)S signatures in all of our study lakes. δ(34)S signatures were highly effective in characterizing these food web relationships. Selenium concentrations in yellow perch were 1.6 times those of its prey, which indicates that Se is biomagnified by this fish in our study lakes. Estimated Se concentrations in yellow perch gonads suggest that in two of our study lakes one-third of fish are at risk of reproductive toxicity.
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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".