Arsenic mobility in a changing northern climate: Implications for geochemical baselines and long-term stability of contaminants in lake systems
Bibliographic record
Abstract
Climate change is influencing the biogeochemical dynamics of lake systems in northern Canada. These changes may affect the loading and cycling of naturally occurring metal(loid)s and the long-term stability of mining-derived contaminants in sub-Arctic lakes. Arsenic (As) concentrations of lakes in the Courageous Lake Greenstone Belt (CLGB), Northwest Territories, Canada, are elevated from the weathering of mineralized bedrock and/or the operation of historical gold mines (Tundra and Salmita mines). In this region, the cumulative effects of resource extraction and modern climate warming make it difficult to discern between anthropogenic impacts and baseline geochemistry. This study integrates As geochemistry, organic petrography, paleoclimate proxies (particle size, organic matter (OM) type and quantity), and radiometric dating (14C and 210Pb) to determine the influence of past and present climate warming on the long-term stability of As in lakes surrounding Tundra Mine. The findings of this study demonstrate baseline As concentrations in lake sediments ranging from 28 to 170 mg·kg−1 (median: 40 mg·kg−1; n = 102) and provide evidence that weathering of mineralized bedrock and terrigenous material provides an ongoing source of naturally derived As to some lakes of the CLGB. An increased accumulation of OM in the near-surface sediment, as a result of climate warming, influences redox dynamics and results in As release from minerals to pore waters via reductive dissolution of As-bearing minerals (i.e. scorodite and Fe-(oxy)hydroxides). Under these changing redox conditions, solid phase OM mediates the diffusion of dissolved As to overlying surface waters by providing a substrate for As sequestration and facilitating the precipitation of authigenic As-bearing minerals (i.e. framboidal pyrite, As-sulphides, Fe-(oxy)hydroxides). However, the effect of these changes will differ between lakes as the long-term stability of As is influenced by the source and primary hosts of As in lake sediments. Knowledge from this study will help predict future climate-driven changes to metal(loid) cycling in sub-Arctic lakes and improve environmental monitoring and remediation strategies at northern metal mines. The possibilities and benefits of weaving traditional knowledge with our research and teaching of geological sciences and engineering are also explored in this thesis, based on hands-on experience gained through this study.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".