Using complementary biomarkers to unravel fish lifetime exposure to hypoxia and mercury
Bibliographic record
Abstract
Aquatic ecosystems are losing oxygen due to climate change. This deoxygenation can favor microbial methylation of mercury (Hg). To understand the dynamics of Hg under increasing deoxygenation, we simultaneously quantified both Hg and hypoxia (<2 mg O2·L−1) lifetime chronologies in fishes. We used a novel combination of chemical biomarkers in ear stones and eye lenses. We compared these markers in two species with different life histories, benthic round goby ( Neogobius melanostomus) and semi-demersal yellow perch ( Perca flavescens), from two connected ecosystems with different levels of hypoxia: the Central Basin of Lake Erie and the less hypoxic but more polluted Western Basin. Overall, Central Basin round goby were exposed to hypoxia throughout their lifetime and exhibited significantly elevated eye lens Hg concentrations ([Hg]) compared to their Western Basin counterparts. In contrast, the Central Basin yellow perch were exposed to hypoxia only at their juvenile stage. Central Basin yellow perch exhibited significantly lower eye lens [Hg] compared to their Western Basin counterparts. Patterns revealed by eye lens [Hg] were not detectable in muscle tissue [Hg]. Findings show that exposure to hypoxia can alter fish lifetime Hg accumulation patterns, with species-specific outcomes.
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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 | 0.001 | 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) | 0.001 | 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".