Microchemical analysis of selenium in otoliths of two West Virginia fishes captured near mountaintop removal coal mining operations
Bibliographic record
Abstract
Otoliths, calcified inner ear structures, were collected from creek chubs (Semotilus atromaculatus) and green sunfish (Lepomis cyanellus) living in mountaintop mining-impacted and reference streams and analyzed for selenium (Se) content using laser ablation-inductively coupled mass spectrometry. Significant differences in otolith Se were found between the 2 fish species. Results from the present study suggest that a retrospective reconstruction of Se concentrations in muscle can be derived from Se concentrations in otoliths in creek chub but not green sunfish, exemplifying the importance of species differences when determining partitioning of Se among specific tissues. Green sunfish otoliths from all sites contained background (<1 μg/g) or low (1-4 μg/g) average concentrations of whole-otolith Se. In contrast, creek chub otoliths from the historically mined site contained much higher (≥5 μg/g) concentrations of Se than for the same species in the unmined site or for the green sunfish. These data suggest that body burdens of Se in fish can vary considerably over time and that both the timing of sampling and species choice could heavily influence Se assessments.
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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 | 0.001 | 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".