Post-depositional mobility of arsenic in a changing climate: implications for cumulative effects assessments at northern mine sites
Bibliographic record
Abstract
Climate change is affecting the seasonality, biological productivity, and hydrology of lakes in high northern latitudes. These changes may affect the cycling of naturally occurring metal(loid)s and long-term stability of mining-derived contaminants. In mineralized regions, where concentrations of naturally occurring metal(loids) are commonly above environmental quality guidelines, understanding the transport and fate of elements and the drivers of chemical change is especially relevant to guide cumulative effects assessments at past, present and future mine sites. This study integrates arsenic geochemistry, organic petrography, multivariate analysis of climate proxies (particle size, organic matter type and quantity), and radiometric dating (14C and 210Pb) to determine the influence of modern and late-Holocene (5,000 yr cal BP to present) warming episodes on the loading and cycling of arsenic in lake sediments. Integrated paleoclimate and sediment geochemistry reconstructions of two sediment cores collected from mining-impacted lakes in the Courageous Lake Greenstone Belt, Northwest Territories, Canada, document increases in sediment and porewater arsenic concentrations coincident with periods of climate warming. The presence of both primary arsenopyrite and secondary, authigenic arsenic-bearing minerals (framboidal pyrite and Fe-oxyhydroxides; determined by SEM, EMPA and synchrotron-based bulk-XANES) suggests that enhanced weathering and active remobilization of geogenic arsenic occurred in lake catchments during past warming intervals. Detailed characterization of the solid-phase speciation of arsenic and its association with organic matter shows that organic material plays an important role in stabilizing redox-sensitive authigenic minerals (i.e., sulphides and Fe-oxyhydroxides) in lake sediments. Based on the results of this study, we expect that increased concentrations of aquatic- and terrestrially-derived labile organic matter will drive the redistribution of arsenic in shallow lake sediments and result in surface-enrichment of arsenic. These findings are relevant for predicting future climate change-driven variations in metal(loid) cycling in sub-Arctic lakes. Knowledge from this study can be used to improve environmental monitoring and remediation strategies at northern metal mines.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".