Contrasting seasonal cycling of arsenic in a series of subarctic shield lakes with different morphometric properties
Bibliographic record
Abstract
The subarctic shield near Yellowknife, Northwest Territories (NWT), is populated with thousands of small lakes (<1.5 km2) and several large lakes. Historic mining activities in the region have left a legacy of environmental impacts and widespread arsenic (As) contamination in both aquatic and terrestrial environments. In particular, several small subarctic lakes near Yellowknife have been previously documented to be contaminated with high levels of As. Subarctic lakes are characterized by seasonal ice-cover that can persist for more than half of the year, yet little is known about the under-ice spatial and seasonal dynamics of As cycling. The objective of this study is to contrast seasonal changes in As cycling within and among a series of lakes with different basin morphologies during ice-cover and into the ice-free seasons. In this study, a combination of data including water profile sampling, sediment cores, snow and ice measurements, and bathymetric mapping were collected in four lakes from November 2020 to October 2021. Continuous monitoring of lake physical properties (dissolved oxygen, temperature, and light) was conducted via data loggers installed at 1 m depth intervals in each lakes’ water column. Detailed profiles of water chemistry were collected monthly at the deepest part of each lake, examining numerous key water chemistry elements with a focus on dissolved and particulate As concentrations. Key results from this study indicated: 1) Distinct seasonal variation in As over the ice-on and open-water periods, 2) The important role of lake mixing regimes in the mobility of As, 3) Field evidence of Fe attenuation of As from the water column. This project contributes important information on the winter cycling of As, which will help to inform our understanding of the chemical recovery of subarctic lakes from As pollution.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".