Selenium and Mercury in Freshwater Fish Muscle Tissue and Otoliths: A Comparative Analysis
Bibliographic record
Abstract
Abstract Evaluating potential ecological and human health risks of exposure to bioaccumulative trace elements is typically implemented using analysis of tissue samples. Increasingly, the microchemistry of fish calcified structures is used to elucidate the lifetime exposure to trace elements. In the present study, we measured total mercury (THg), methylmercury (MeHg), and selenium (Se) in muscle tissue and otolith samples from 12 species of fish collected at reference sites and locations influenced by power plant wastewater. Muscle tissue concentrations of Se were sensitive to recent wastewater exposure magnitude, stream type, trophic level, and species (p < 0.001). For Hg, concentrations in muscle tissue and otoliths were affected only by trophic level and species. Levels of THg and Se in muscle tissue and otolith samples were positively correlated for those species with a robust sample size. Some individual fish from 3 species (channel catfish, hybrid striped bass, and freshwater drum) showed significantly increasing or decreasing lifetime concentrations of either THg or Se in otolith samples. Multiple regression analysis indicated that for bluegill muscle tissue Se concentrations could be best explained utilizing water concentrations of selenium, sulfate, and molybdenum (r2 = 0.87; p < 0.001). Because of the increased cost and specialized sample processing requirements of analyzing trace elements in otolith structures, it may be prudent to limit these analyses to those species where insights into temporal trends are sought or where evidence indicates that fish move into or out of contaminated water bodies. Environ Toxicol Chem 2019;38:1467–1475. © 2019 SETAC
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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 | 0.001 |
| 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".