Yukon Dust from Source to Sink: Characterizing the St. Elias Mountains as a High-Latitude Dust Source
Bibliographic record
Abstract
High-latitude dust is an important, yet poorly understood, component of Earth’s climate system. It impacts weather and albedo by acting as cloud condensation nuclei, directly affects Earth’s radiative balance, and can enhance productivity by supplying nutrients to ecosystems. In order to better understand high-latitude dust dynamics and impacts, we sampled glacial meltwater, surface sediment, and Holocene loess in the Łhù’ààn Mân (Kluane Lake) region of the Yukon Territory, Canada. We found that suspended sediment concentration is correlated with average slope and glaciation within a catchment, and is influenced by river morphology. We present new εNd and 87Sr/86Sr values that define a broader isotopic field than previously measured in the Yukon Territory, and demonstrate a strong relationship between average bedrock age and εNd on a catchment-by-catchment basis throughout the St. Elias Mountains. Our 206Pb/207Pb and 208Pb/207Pb data further define the provenance fingerprint and provide evidence of Asian pollution aerosols in the Yukon Territory. Altogether, these data add to a growing catalog of high-latitude dust-producing regions, offering insights into dust dynamics and transport patterns in the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".