Bibliographic record
Abstract
The increasing frequency and intensity of wildfires, exacerbated by climate change, pose a significant threat to both the environment and human health. In addition to destroying ecosystems, these fires cause severe air pollution, particularly through fine particulate matter (PM2.5, diameter ≤ 2.5 µm), which can be transported over long distances. A recent example are the wildfires in Canada, whose smoke enveloped New York City in dense smog. Fine particulate matter increases the risk of cardiovascular and respiratory diseases, especially among vulnerable groups such as children, pregnant women, individuals with preexisting conditions, and the elderly. Globally, emissions from wildfires are linked to hundreds of thousands of premature deaths annually. Protective measures such as early warning systems, air filtration systems, and the use of masks can help reduce exposure. However, knowledge gaps remain, particularly regarding the specific components of pollutants and their interactions with environmental factors. Long-term research is essential to better understand the health impacts and to develop targeted prevention strategies. Wildfires underscore the urgent need for global climate protection measures and innovative approaches to public health preparedness.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.059 |
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; both teacher heads agree on what is shown here.
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".