How arsenic contamination influences downslope wetland plant and microbial community structure and function
Bibliographic record
Abstract
Mine tailings are prevalent worldwide and can adversely impact adjacent ecosystems, including wetlands. This study investigated the impact of gold (Au) mine tailings contamination on peatland soil and pore water geochemistry , vegetation and microbial communities , and microbial carbon (C) cycling. Maximum arsenic (As) concentrations in peat and pore water reached 20,137 mg kg −1 and 16,730 μg L −1 , respectively, but decreased by two orders of magnitude along a 128 m gradient extending from the tailings into the wetland. Carbon and other macronutrient (N, P, K) concentrations in peat and pore water significantly increased with distance from contamination. Relative percent cover and species richness of vascular and non-vascular plants significantly increased with distance into the wetland, with higher non-vascular richness being found at intermediate distances before transitioning to a vascular plant dominated community. Bacterial and archaeal community composition exhibited a decreased proportion of members of the phylum Acidobacteria (notably of the order Acidobacteriales) and increased diversity and richness of methanogens across a larger range of orders farther from the tailings source, an indication of microbial C-cycling potential. Consistent with changes in microbial communities , in vitro microbial CH 4 production potential significantly increased with distance from the contaminant source. This study demonstrates both the profound negative impact that metalliferous tailings contamination can have on above and belowground communities in peatlands, and the value of wetland preservation and restoration.
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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) | 0.000 | 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".