Selenium incorporation in fish otoliths: effects of selenium and mercury from the water
Bibliographic record
Abstract
To study fish migration using otolith microchemistry, it is important to understand the relationship between elements in the otoliths and in the surrounding water, including potential interactions with other elements. Selenium (Se) is a trace element with strong affinity for mercury (Hg). To test if Se is a reliable tracer for fish migration, the effects of dissolved Se and Hg concentrations on Se incorporation in fish otoliths were investigated experimentally. Brown bullheads ( Ameiurus nebulosus ) were reared in waters spiked with various concentrations of inorganic Se and Hg. Otolith Se:Ca increased nonlinearly with dissolved Se concentrations as there was no significant difference between fish reared in low and medium [Se] waters (Se:Ca for low [Se] waters, 7.64 × 10−6; medium, 6.59 × 10−6; high, 1.24 × 10−5). Our study also provided the first evidence of a negative effect of Hg on Se incorporation into otoliths (p = 0.01), a phenomenon most evident in high [Se] waters. Because of the influence of Hg, caution should be taken when inferring fish migration based on Se.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".