Air Pollution's Effects on the Human Respiratory System
Bibliographic record
Abstract
The World Health Organization defines air pollution as "any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere." The most common pollutants include particulate matter, carbon monoxide, ozone, nitrogen oxide, and sulfur dioxide. The two types of air pollution, indoor and ambient, both contribute to a host of cardiac and respiratory illnesses. Exposure to excess levels of air pollution is significantly associated with a variety of acute and chronic respiratory illnesses, such as chronic obstructive pulmonary disease, asthma, respiratory allergies, and lung cancer. The effects of air pollution disproportionately impact the extremes of the age distribution, perhaps due to altered immune responses. Athletes and those who exercise outdoors are at greater risk for the respiratory effects of air pollution. This article discusses the epidemiology, types of respiratory diseases, and mechanisms involved in exposure to excess levels of air pollution. Biomedical engineering can contribute to the identification of air pollutants through the design of novel instrumentation using materials based on nanotechnology. Mathematical models can also be developed to characterize the physiological effects of air pollution.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".