Predictive meta-regressions relating mercury tissue concentrations of freshwater piscivorous mammals
Bibliographic record
Abstract
Mercury (Hg) is a pollutant of global concern. Sentinel species such as river otter (Lontra canadensis) and mink (Neovison vison) are often used to monitor environmental concentrations in freshwater ecosystems. Tissue total Hg (THg) concentrations are frequently used as biomarkers of exposure. However, there is no comprehensive model relating Hg tissue concentrations in different tissues, making interstudy comparisons challenging. Our objective was to establish conversion factors relating fur, brain, liver, kidney, and muscle THg concentrations using mean concentrations and standard errors reported in the literature. We used data from more than 6000 samples, pooled across 16 studies and 96 sampling sites in North America and Europe. Sixteen regressions were derived for the river otter and mink models, which were statistically significant at a 95% confidence interval and yielded high explained variances. The models were validated using an external data set of individually measured THg tissue concentrations. The validated conversions were used to evaluate the current fur Hg screening guidelines of 20 µg/g and 30 µg/g. At both of these fur concentrations, brain concentrations are of concern for altering brain neurochemistry. We suggest a more conservative fur Hg screening guideline of 15 µg/g to protect sensitive furbearers. The conversion factors can be used to predict internal organ THg concentrations from fur measurements, eliminating the need for invasive tissue sampling. Environ Toxicol Chem 2017;36:2377-2384. © 2017 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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.016 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".