Elimination of Mercury by Yellow Perch in the Wild
Bibliographic record
Abstract
The rate of methylmercury (MeHg) elimination by fish is important in determining the extent of bioaccumulation and for predicting recovery times of MeHg-contaminated fisheries. Rates of MeHg elimination remain uncertain in existing bioaccumulation models due to a lack of field studies. We addressed this problem by monitoring fish that had naturally accumulated isotopically enriched MeHg (spike MeHg) during a whole-ecosystem experiment. We transported yellow perch (Perca flavescens) from the experimental lake to an untreated lake and monitored spike total mercury (THg, most of which was MeHg) losses over 440 d. Spike THg was distributed among fish tissues in a similar way as ambient THg (background non-spike THg). We observed rapid loss of spike THg from liver and other visceral tissues (approximately 90 d) followed by a plateau. Subsequently, there was prolonged redistribution of spike THg into muscle (180 d). Loss of spike THg from the whole fish occurred > 5 times slower (half-life of 489 d) than in past laboratory studies using this species. We determined that MeHg bioaccumulation models with laboratory-based elimination rates produced faster losses than those observed in wild fish. The present findings provide support for refining elimination rates in MeHg models and show the importance of examining biological processes under natural conditions.
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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".