Bibliographic record
Abstract
Abstract. The ’A’ą̈y Chù’ Valley in Kluane National Park, Yukon, Canada has undergone significant hydrological change in the past decade due to climate-driven glacial recession. This has reverted the ’A’ą̈y Chù’ to a major source of sediment-derived mineral dust, representing an environmental change for the region. Mineral dust influences climatic radiative forcing and impacts human health, both of which depend on its concentration, size distribution, and composition. This work discusses results from a field campaign conducted in the ’A’ą̈y Chù’ Valley in 2021 aimed at understanding and quantifying these parameters, with comparison to a previous campaign in the same location to evaluate the evolution of the dust emissions between 2018 and 2021. An optical particle counter (OPC) instrument measured a mean volume diameter of airborne dust of 4.43 µm at 3.3 m above ground, with Coulter Counter measurements being used for comparison and validation. The concentration of many metal(loid)s in the dust were also studied: Al, Ag, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, Pb, Rb, Tl, U, V, and Zn. It was found that 24-hour ambient air quality criteria for exposure to several metal(loid)s were surpassed. Significant enrichment of several metal(loid)s was observed for both the PM10 and PM2.5 size fractions relative to the Total Suspended Material (TSP) fraction of the mineral dust. This suggests that the mineral dust in the ’A’ą̈y Chù’ Valley possesses compounding characteristics that are detrimental to human health due to exposure to potentially toxic metal(loid) concentrations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.463 | 0.319 |
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".