Grain-Scale Analysis of Solid-Phase Organic Matter and Arsenic Mobility in Mining-Impacted 1 Sediment from Sub‐Arctic Lakes, Northwest Territories, Canada
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 sulphides. The results of this study demonstrate that the accumulation of solid-phase 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. 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 (cutinite; funginite). Most As is hosted by fine-grained Fe-(oxy)hydroxides or sulphide minerals ( e.g., goethite, orpiment, lepidocrocite, 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), 19 % 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 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 | 0.001 | 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".