Screening-level risk assessment of methylmercury for non-anadromous Arctic char (<i>Salvelinus alpinus</i>)
Bibliographic record
Abstract
Non-anadromous forms of Arctic char (Salvelinus alpinus), those that are restricted to lakes and rivers, typically have higher mercury (Hg) concentrations than anadromous forms, which migrate to and from the sea. Using tissue burden data from the literature and our own analyses, we performed a screening-level risk assessment of methylmercury (MeHg) for non-anadromous Arctic char. Our assessment included 1569 fish distributed across 83 sites. Site-specific mean total Hg concentrations in non-anadromous Arctic char muscle varied considerably from 0.01 to 1.13 µg/g wet weight, with 21% (17 of 83 sites) meeting or exceeding a threshold-effect level in fish of 0.33 µg/g wet weight, and 13% (11 of 83 sites) meeting or exceeding a threshold-effect level in fish of 0.5 µg/g wet weight. Of the sites in exceedance of the 0.33-µg/g threshold, 7 were located in Greenland and 10 in Canada (Labrador, Nunavut, and Yukon). All but one of these sites were located in interfrost or permafrost biomes. Maximum total Hg concentrations exceeded 0.33 µg/g wet weight at 53% of sites (40 of the 75 sites with available maximum Hg values), and exceeded 0.5 µg/g wet weight at 27% (20 of 75 sites). Collectively, these results indicate that certain populations of non-anadromous Arctic char located mainly in interfrost and permafrost regions may be at risk for MeHg toxicity. This approach provides a simple statistical assessment of MeHg risk to non-anadromous Arctic char, and does not indicate actual effects. We highlight the need for studies that evaluate the potential toxic effects of MeHg in non-anadromous Arctic char, as well as those that aid in the development of a MeHg toxic-effect threshold specific to this species of fish. Environ Toxicol Chem 2019;38:489-502. © 2018 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".