Transport of pollutants from eastern Colorado into the Rocky Mountains via upslope winds
Bibliographic record
Abstract
The confluence of mountain meteorology and major pollution sources come together to transport pollutants across the Front Range, especially nitrogen species (NH3, NH4+, orgN, NO3-, and HNO3) from agricultural and urban regions, into the Rocky Mountains. The focus of this study was to examine the meteorological conditions in which atmospheric wet deposition of inorganic nitrogen in the Rocky Mountains was anomalously high. We analyzed 19 years (1994-2013) of precipitation and concentrations of wet inorganic nitrogen data from three National Atmospheric Deposition Program (NAPD) sites in the Rocky Mountains: Beaver Meadows (CO19), Loch Vale (CO98), and Niwot Ridge (CO02). Beaver Meadows (2477 m), Loch Vale (3159 m), and Niwot Ridge (3520 m) are all within 40 km but differ in elevation, resulting in different seasonal precipitation composition and totals. The North American Regional Reanalysis (NARR) was used to observe synoptic conditions that influenced two high wet deposition events from August 18-20, 2006 and July 6-8, 2012. Interestingly, anti-cyclones in southern Canada and high precipitable water values associated with monsoonal flow played significant roles in initiating convection that caused high values of wet deposition of inorganic nitrogen in the Rocky Mountains. The Advanced Research WRF model was then used to simulate the meteorology at a high spatial and temporal resolution for the two time periods to examine the contribution of cloud-scale convection to wet nitrogen deposition in the Rocky Mountains. A mesoscale mountain circulation caused by differential heating between mountains slopes and the plains was the main driver of the slow westward transport towards the mountains while cloud-scale convection contributed greatly to the transport of nitrogen along the Colorado Front Range.
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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 | 0.000 |
| 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".