Mesoscale Impacts on Cold Season PM<sub>2.5</sub> in the Yukon Flats
Bibliographic record
Abstract
Near-surface PM2.5 and meteorological observations were performed in three rural communities in the high latitude Yukon Flats valley at various times during the cold season (October to April). These data were synthesized with data from other meteorological sites, NCEP reanalysis and MAIAC retrieved aerosol optical depths data to analyze the role of mesoscale processes and radiation on air quality. Under weak large-scale forcing mountain-valley circulations develop that are driven by the differences in insolation. During the long dark nights, radiative cooling occurs in the near-surface layer of the mountain slopes of the Brooks, Ogilvie and White Mountains Ranges and at the bottom of the valley. Here surface-based inversions (SBI)—known as roof-top inversions—forms, while the cold air drains from the slopes. A frontal wedge forms when the cold air slides over the relatively colder air in the valley. Drainage of cold air from the Brooks Range governed the circulation and cold air pooling in the valley. Concentrations during times with and without SBI differed significantly (at 95% confidence) at two sites indicating that local emissions were the major contributor. At the site, which is closest to the mountains, concentrations marginally changed in the presence of inversions. At all sites, 24-h mean PM2.5 remained below the National Ambient Air Quality Standard.
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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 | 0.001 |
| 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) | 0.001 | 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".