Evaluation of the Airborne Particles Fraction Responsible for Adverse Health Effects
Bibliographic record
Abstract
A new approach to evaluation of the fraction of airborne particles that is responsible for adverse health effects has been developed. In this approach a two-fraction concept for the adverse health effects is proposed. This concept considers aerosol particles in the air, the fraction of particles deposited in the respiratory system (captured by the system) and the fraction of particle accumulated in the body (captured by the body). The latter fraction of airborne particles actually causes the adverse health effects and associated with the health risk. The approach has been applied to workers in crystal glass industry exposed to lead. A size resolved sampling technique was employed to characterize nanoparticles at work places at several plants involved in lead processing. Lead aerosol size distributions were obtained across the entire aerosol particle size range from 1 nm to 30 μm (diameter). Size distributions were used to calculate the total lead intake due to particle deposition in the respiratory system of workers. A bio-kinetic model was employed to evaluate various size fraction contributions to the total blood lead level in workers. A comparison was made between calculated blood lead levels and actual measurements of blood lead levels of workers exposed to atmospheres containing lead. It was found that nanoparticles cause the major health risk due to high deposition efficiency and low clearance rate by the mucociliary escalator.
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 | 0.021 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".