Zn Isotope Fractionation in the Oyster <i>Crassostrea hongkongensis</i> and Implications for Contaminant Source Tracking
Bibliographic record
Abstract
Variations in stable isotope ratios have been used to trace sources of contaminants as well as their biogeochemical pathways in the environment. In this study, we investigated the influences of internal redistribution among tissues and ambient water conditions on Zn isotope fractionation in oysters. There was no significant difference in Zn isotope ratios during in vivo Zn transportation among various oyster tissues. Estuarine oysters were exposed to additional Zn either at different salinities or at different Zn concentrations, following which the Zn isotope ratios in the oysters were measured. Results showed no significant difference in δ 66/64 Zn values in the oysters exposed at different salinities. Tissue Zn accumulation increased with increasing Zn levels in water over the 30 day exposure. Within this period, there was a nearly 0.3‰ difference in averaged δ 66/64 Zn values in the exposed oysters compared to the initial δ 66/64 Zn values in the oysters prior to exposure. However, there was no evidence of significant difference in δ 66/64 Zn values in oysters exposed at different Zn levels, with postexposure signatures similar to the δ 66/64 Zn values of the Zn solution added. Our results suggested that the δ 66/64 Zn values measured in the oysters were approaching the δ 66/64 Zn values of the “source” faster with increasing Zn concentrations added in the water. This study highlighted the absence of Zn isotope fractionation during Zn internal distribution and in vivo transport in oysters. The calculation of the contributions of different Zn sources demonstrated that oysters can be a sentinel animal for Zn source tracking in marine environments.
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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".