Hydrologic control on winter dissolved oxygen mediates arsenic cycling in a small subarctic lake
Bibliographic record
Abstract
Abstract The seasonal development of an ice cover is a characteristic feature of subarctic lakes, yet the biogeochemical cycling of redox sensitive elements under ice, including arsenic (As), is poorly understood. We conducted comprehensive geochemical characterization of lake waters, sediment pore waters and lake sediments over two consecutive years to develop a conceptual model of As, iron (Fe), and sulfur (S) dynamics under ice in a shallow subarctic lake (mean depth 2.0 m) impacted by more than 60 yr of As pollution from local gold mining emissions. Lake sediments were a source of As to overlying waters during both winters when oxygen was depleted from interfacial sediments through the reductive dissolution of As‐bearing Fe (oxy)hydroxides, but the influence on lake water chemistry was distinctly different between years and dependent on winter hydrology of the lake. When the lake was hydrologically disconnected from the upstream watershed, anoxia developed through the entire water column and high concentrations of As (> 100 μ g L −1 ) and Fe (> 1000 μ g L −1 ) were measured in lake water. During the second winter, open‐water flow persisted at the lake inlet, which replenished dissolved oxygen in the under‐ice water column, suppressed the upward migration of the Fe and SO 4 redox boundaries, and limited sediment As efflux. These findings demonstrate how changing hydrology, specifically ice cover duration, and hydrological connectivity can influence the winter cycling of As, Fe, and S in shallow subarctic lakes.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".