Generation and characterization of TiO2 nano-aerosols of different agglomeration states
Bibliographic record
Abstract
It is now widely recognized that exposure to combustion related outdoor air pollution is a contributing risk factor to the exacerbation of cardiopulmonary disease and death at concentrations currently observed in Canada.Pollution in both the particle and gas phase has been linked to health effects.Atmospheric pollutants covary in both space and time due to common sources and meteorology.The independent role of individual pollutants and sources of pollution have thus not been clearly identified, making the development of cost-effective mitigation strategies challenging.Epidemiological approaches to understanding the role of atmospheric pollutant mixtures will be discussed.Some recent results based on studies of both short and long term exposure on mortality are presented.Richard Thomas Burnett received his Ph.D. from Queen's University in 1982 in Mathematical Statistics.He is a senior research scientist with the Healthy Environments and Consumer Safety Branch of Health Canada, where he has been working since 1983 on issues relating to the health effects of outdoor air pollution.Dr. Burnett work has focused on the use of administrative health and environmental information to determine the public health impacts of combustion related pollution using non-linear random effects models, time series and spatial analytical techniques. Prediction of cytochrome P450-based drug-drug interactions from in vitro information.
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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 | 0.000 | 0.000 |
| 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".