Large-scale monitoring of air pollution in remote and ecologically important areas
Bibliographic record
Abstract
New advances in air quality monitoring techniques, such as passive samplers for nitrogenous (N) or sulphurous (S) pollutants and ozone (O 3 ), have allowed for an improved understanding of concentrations of these pollutants in remote areas.Mountains create special problems with regard to the feasibility of establishing and maintaining air pollution monitoring networks, due to their complex topography and difficult access.Therefore, careful design of monitoring networks, selection of monitoring equipment, and a reliable workforce are essential for successful mountain monitoring campaigns.The USDA Forest Service team, in collaboration with various partners in Europe and North America, has conducted numerous monitoring campaigns in order to determine concentrations of O 3 , nitrogen dioxide (NO 2 ), ammonia (NH 3 ), nitric acid vapor (HNO 3 ), and sulphur dioxide (SO 2 ) in remote areas.These results, aided by geostatistical methodologies, have resulted in the creation of maps that are essential for a better understanding of the distribution of various air pollutants in the Carpathian Mountains (specifically, the Tatras, Retezat, and Bucegi ranges) in Europe; the Sierra Nevada (including Sequoia, Kings Canyon and Yosemite National Parks), the San Bernardino Mountains, the White Mountains, and Joshua Tree National Park in California; the Columbia Rivers Basin in Oregon; and the Athabasca Oil Sands Region in northern Alberta, Canada.Information on the concentrations and distribution of air pollutants which have been measured in those areas provides an understanding of their potential risks to human health, ecosystem health and sustainability, and ecosystem services.
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 | 0.000 | 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".