Occurrence of Arsenic in Nearshore Aquifers Adjacent to Large Inland Lakes
Bibliographic record
Abstract
Metal oxides that form near sediment-water interfaces in marine and riverine settings are known to act as a sediment trap for pollutants of environmental concern (e.g., arsenic and mercury). The occurrence of these pollutant traps near sediment-water interfaces in nearshore lake environments is unclear yet important to understand because they may accumulate pollutants that may be later released as environmental conditions change. This study evaluates the prevalence of pollutant sediment traps in nearshore aquifers adjacent to large lakes and the factors that affect the accumulation and release of pollutants, specifically arsenic. Field data from six sites along the Laurentian Great Lakes indicate widespread enrichment of arsenic in nearshore aquifers with arsenic sequestered to iron oxide phases. Arsenic enrichment at all sites (solid-phase arsenic >2 μg/g) suggests that this is a naturally occurring phenomenon. Arsenic was more mobile in reducing aquifers with elevated dissolved arsenic (up to 60 μg/L) observed, where reducing groundwater mixes with infiltrating oxic lake water. Dissolved arsenic was low (<3 μg/L) in all oxic nearshore aquifers studied despite high solid-phase arsenic concentrations. The findings have broad implications for understanding the widespread accumulation of reactive pollutants in nearshore aquifers and factors that affect their release to large 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".