Impacts of mineral dust on soils and vegetation at Lù’àn Mân (Kluane Lake), Yukon Territory).
Bibliographic record
Abstract
Dust is a major aerosol in the atmosphere. Atmospheric dust originates from human activities or natural processes and the deposition of dust affects several ecological and biogeochemical processes. Lù’àn Mân (61°13’03’’ N, 138°37’34’’ W) is located between the Ruby Ranges on the east and the Kluane Ranges in the St. Elias mountains on the west, and on the traditional lands of Kluane, Champagne-Ashihik, and White River First Nations. Kaskawulsh Glacier, located 25 km from the A’ą̈y Chù (formerly the Slims River) delta, began to retreat in the nineteenth century and this retreat accelerated in the late twentieth and early twenty-first centuries. In 2016, Slims Lake had partially drained, leading the water to be re-routed from A’ ą̈y Chù into Kaskawulsh River. Therefore, the level of Lù’àn Mân fell, and the drying of the riverbed became an important source of aeolian sediments and important dust storms were observed. We studied dust and trace elements deposition in the area in lichens and soils. The objective of this study was to determine the impacts of dust deposition on trace elements concentrations in vegetation and soils along a deposition gradient. To do this, we sampled lichens (Peltigera canina) and soils at sixty sites in the zone affected by the dust storms. We analyzed six trace elements (Ni, Cu, Zn, As, Cd, Pb) by ICP-MS. The results showed that the sites close to the delta had higher trace element concentrations than the sites 10 and 20 km away.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".