Mercury in freshwater ecosystems of the Canadian Arctic: Recent advances on its cycling and fate
Bibliographic record
Abstract
The Canadian Arctic has vast freshwater resources, and fish are important in the diet of many Northerners. Mercury is a contaminant of concern because of its potential toxicity and elevated bioaccumulation in some fish populations. Over the last decade, significant advances have been made in characterizing the cycling and fate of mercury in these freshwater environments. Large amounts of new data on concentrations, speciation and fluxes of Hg are provided and summarized for water and sediment, which were virtually absent for the Canadian Arctic a decade ago. The biogeochemical processes that control the speciation of mercury remain poorly resolved, including the sites and controls of methylmercury production. Food web studies have examined the roles of Hg uptake, trophic transfer, and diet for Hg bioaccumulation in fish, and, in particular, advances have been made in identifying determinants of mercury levels in lake-dwelling and sea-run forms of Arctic char. In a comparison of common freshwater fish species that were sampled across the Canadian Arctic between 2002 and 2009, no geographic patterns or regional hotspots were evident. Over the last two to four decades, Hg concentrations have increased in some monitored populations of fish in the Mackenzie River Basin while other populations from the Yukon and Nunavut showed no change or a slight decline. The different Hg trends indicate that the drivers of temporal change may be regional or habitat-specific. The Canadian Arctic is undergoing profound environmental change, and preliminary evidence suggests that it may be impacting the cycling and bioaccumulation of mercury. Further research is needed to investigate climate change impacts on the Hg cycle as well as biogeochemical controls of methylmercury production and the processes leading to increasing Hg levels in some fish populations in the Canadian Arctic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".