Mediation of arsenic mobility by organic matter in mining-impacted sediment from sub‐Arctic lakes: implications for environmental monitoring in a warming climate
Bibliographic record
Abstract
Abstract Arsenic (As) is commonly sequestered at the sediment–water interface (SWI) in mining-impacted lakes through adsorption and/or co-precipitation with authigenic iron (Fe)-(oxy)hydroxides or sulfides. The results of this study demonstrate that the accumulation of organic matter (OM) in near-surface sediments also influences the mobility and fate of As in sub-Arctic lakes. Sediment gravity cores, sediment grab samples, and porewaters were collected from three lakes downstream of the former Tundra gold mine, Northwest Territories, Canada. Analysis of sediment using combined micro-X-ray fluorescence/diffraction, K-edge X-ray Absorption Near-Edge Structure (XANES), and organic petrography shows that As is associated with both aquatic (benthic and planktonic alginate) and terrestrially derived OM (e.g., cutinite, funginite). Most As is hosted by fine-grained Fe-(oxy)hydroxides or sulfide minerals (e.g., goethite, orpiment, lepidocrocite, and mackinawite); however, grain-scale synchrotron-based analysis shows that As is also associated with amorphous OM. Mixed As oxidation states in porewater (median = 62% As (V), 18% As (III); n = 20) and sediment (median = 80% As (-I) and (III), 20% As (V); n = 9) indicate the presence of variable redox conditions in the near-surface sediment and suggest that post-depositional remobilization of As has occurred. Detailed characterization of As-bearing OM at and below the SWI suggests that OM plays an important role in stabilizing redox-sensitive authigenic minerals and associated As. Based on these findings, it is expected that increased concentrations of labile OM will drive post-depositional surface enrichment of As in mining-impacted lakes and may increase or decrease As flux from sediments to overlying surface waters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.003 | 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 teacher head, 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".