Bibliographic record
Abstract
Air pollution can negatively impact public and individual health, as well as the environment. This paper investigates the impact of wildfires on levels of different air pollutants in order to determine whether focusing on reducing wildfires can be an efficient way to reduce specific pollutants. By plotting the number of acres burning in wildfires along with the overall concentration of particulate matter less than 2.5 microns in diameter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) during a period of time in California, this paper estimates the contribution of wildfire smoke to the level of each pollutant. The results show that PM2.5 levels were the most heavily impacted by wildfires out of the four pollutants studied, and therefore actions to reduce the size and quantity of wildfires can be helpful to reduce the damage done by PM2.5 pollution. However, CO, NO2, and SO2 levels, while they may be correlated with fire size, are likely more strongly influenced by other factors. To minimize the harmful effects of these three pollutants, it may be more effective to focus on investigating other sources of CO, NO2, and SO2, such as the burning of fossil fuels in industrial machinery and motor vehicles.
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) | 0.002 | 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".