Watershed hydrology mediates the recovery of an arsenic impacted subarctic landscape
Bibliographic record
Abstract
A holistic understanding of the chemical recovery of lakes from arsenic (As) pollution requires consideration of within-lake biogeochemical cycling of As and processes occurring in the surrounding catchment. This study used a watershed mass balance approach, complemented by experimental sediment incubations, to assess the mobility and transport of As within a subarctic watershed (155 km2) impacted by more than 60 years of atmospheric mining emissions. The period of record spanned a transition from drought to high streamflow between September 2017 and September 2019, which yielded insights into the interacting effects of hydrology and within-lake biogeochemical cycling of As. Internal loading of As from contaminated lake sediments (25–46 kg As year−1) and contributions from terrestrial sources (16–56 kg As yr−1) continue to negatively impact lake water quality (19–144 μg As L−1), but the relative importance of these loads varies seasonally and inter-annually in response to changing hydrological conditions. Wet conditions resulted in greater transport of As from terrestrial reservoirs and upstream areas, shorter lake water retention time, and increased the downstream export of As. During dry periods, the lake was disconnected from the surrounding watershed resulting in limited terrestrial contributions and longer lake water residence time, which delayed recovery due to the greater relative influence of internal loading from contaminated sediments. This study highlights that changing hydroclimatic regimes will alter trajectories of chemical recovery for arsenic impacted lakes through the coupling of within-lake and watershed transport processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".