Drivers of Mercury Cycling in the Rapidly Changing Glacierized Watershed of the High Arctic’s Largest Lake by Volume (Lake Hazen, Nunavut, Canada)
Bibliographic record
Abstract
Across the Arctic, glaciers are melting and permafrost is thawing at unprecedented rates, releasing not only water to downstream aquatic systems, but also contaminants like mercury, archived in ice over centuries. Using concentrations from samples collected over 4 years and calibrated modeled hydrology, we calculated methylmercury (MeHg) and total mercury (THg) mass balances for Lake Hazen, the world’s largest High Arctic lake by volume, for 2015 and 2016. Glacial rivers were the most important source of MeHg and THg to Lake Hazen, accounting for up to 53% and 94% of the inputs, respectively. However, due to the MeHg and THg being primarily particle-bound, Lake Hazen was an annual MeHg and THg sink. Exports of MeHg and THg out the Ruggles River outflow were consequently very low, but erosion and permafrost slumping downstream of the lake increased river MeHg and THg concentrations significantly before entering coastal waters in Chandler Fjord. Since 2001, glacial MeHg and THg inputs to Lake Hazen have increased by 0.01 and 0.400 kg yr -1, respectively, in step with dramatic increases in glacial melt. This study highlights the potential for increases in mercury inputs to arctic ecosystems downstream of glaciers despite recent reductions in global mercury emissions.
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 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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".