Impacts of Mineral Dust on Trace Element Concentrations (As, Cd, Cu, Ni and Pb) in Lichens and Soils at Lhù’ààn Mân’ (Yukon Territory, Canada)
Bibliographic record
Abstract
Abstract Dust is a mineral aerosol of the atmosphere that often contains trace elements such as As, Cd, and Pb. Lhù’ààn Mân’ (Kluane Lake), located in southwestern Yukon, is a region of frequent dust activity. In 2016, the lake level fell due to a dramatic decrease in inflow from glacier meltwater, and the delta of the lake became an important source of dust to surrounding ecosystems. To determine the impacts of dust deposition on vegetation and soil trace element concentrations and characteristics, we sampled the lichen Peltigera canina and soil layers at 57 sites along a deposition gradient located 1.4–33.6 km downwind from the principal dust source. Arsenic, Cd, Cu, Ni and Pb in lichens were negatively correlated with the distance away from the dust source, with the highest correlations in Ni and Pb (r2 = 0.50 and 0.48, respectively). Lichen and tree abundances were negatively impacted by dust deposition, suggesting that dust can affect ecosystem vegetation composition. Starting 8 km away from the dust source, the concentrations of As, Ni, and Pb decreased by more than 50% per km, while Cd and Cu concentrations decreased by more than 40% per km. Overall, within the sampled ecosystems, soil pH is 1.4 times higher in the first 8 km from the dust source while carbon content and nutrients are lower, which implies changes in nutrient availability and cycling in dust‐affected ecosystems.
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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".