Spatial distribution and speciation of mercury in a recovering deepwater redfish (Sebastes mentella) population from St. Lawrence Estuary and Gulf, Canada
Bibliographic record
Abstract
Mercury (Hg) pollution poses a significant threat to the environment, particularly in the form of methylmercury (MeHg). However, little is known about the distribution and influencing factors of Hg in deep-sea (>200m) fish, which is crucial for assessing potential health risks to fish and humans. In Canada, the deepwater redfish (Sebastes mentella) has been designated as an endangered species. After a 25-year fishing moratorium, the redfish population in the St. Lawrence Estuary and Gulf is recovering, and resuming of commercial fishing and human consumption are expected. This study aimed to investigate the distribution of MeHg and total Hg (THg) in the muscle of redfish, as well as the factors influencing its distribution, and to assess the potential human health risks associated with redfish consumption. The redfish samples (n = 123) were collected by Fisheries and Oceans Canada in 2019. The concentrations of THg and MeHg in redfish muscle were determined to be 93.3 ± 183 ng/g (mean ± SD, wet weight) and 78.2 ± 149 ng/g, respectively. Large redfish (>30 cm) accumulated 20 to 30 times more Hg than small redfish (17–30 cm). Small redfish from the Estuary-Western Gulf had higher levels of MeHg and THg than those from the Laurentian Channel and the Northeast Gulf, but the Hg availability to redfish among the three areas were similar. Significant predictors of MeHg concentrations in redfish muscle were determined to be fish length, muscle moisture, δ15N, and N%. MeHg consumption by the general population with an average fish consumption rate is not anticipated to have adverse effects. This study establishes a baseline for future Hg monitoring in the deep water environments in this region. Further research is required to elucidate the cause-effect relationships between various environmental and biological parameters that influence Hg accumulation in deep-sea biota.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".