Foodweb dynamics affect arsenic speciation and bioaccumulation in lakes affected by gold mines
Bibliographic record
Abstract
We investigated the bioaccumulation patterns of arsenic species in freshwater food webs from three lakes near historical mining operations in the Northwest Territories, Canada. Two of these lakes (Long Lake and Lower Martin Lake) were located within 5 km of the mine’s roaster stacks, while a third lake (Small Lake), situated 27 km away, served as a far-field reference site. In each lake, we measured the concentrations of arsenic species, including As(III), As(V), MMA, DMA and organic arsenobetaine, AsB, across multiple environmental and biological compartments. including water, sediment, macrophytes, periphyton, phytoplankton, zooplankton, benthic invertebrates, and small- and large-bodied fish. Across all lakes, total arsenic and inorganic arsenic (As(III) and As(V)) concentrations were inversely related to an organism’s trophic position as determined by δ 15 N. This trend likely reflects the biotransformation of inorganic arsenic to AsB within tissues as well as increased dietary intake of AsB-rich prey, which facilitates As elimination. Our findings suggest that trophic position is a key determinant of inorganic arsenic bioaccumulation, explaining 39-89% of inorganic As bioaccumulation. • We examined arsenic distributions near a point source of contamination. • As(III), As(V), MMA, DMA and arsenobetaine were measured in three aquatic foodwebs. • Total arsenic, As(III), and As(V) were inversely related to trophic position. • Trophic position alone determines most variation of arsenic in these food webs. • Benthic and pelagic connectivity does not appear to influence arsenic concentrations.
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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 | 0.001 | 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".