What we Know Now That We Did Not Know Then and the Way Forward for Burden Estimation
Bibliographic record
Abstract
S12: Estimating the Global Risk and Burden of Particulate Air Pollution Exposure: Issues and Challenges, Beatrix Theater, August 28, 2019, 10:30 AM - 12:00 PM Symposium #76: Estimating the Global Risk and Burden of Particulate Air Pollution Exposure: Issues and Challenges What we Know Now That We Did Not Know Then and the Way Forward for Burden Estimation. Richard T. Burnett, Ph.D. Population Studies Division, Health Canada Interest in estimation the global burden of disease from exposure to outdoor concentrations of fine particulate matter has evolved from simplistic risk models based on a few studies to a complex integration of multiple independent particulate sources based on nearly one hundred studies. These models can now be used to estimate burden from not only outdoor air pollution sources covering the global range, but to other particulate sources including second hand smoke and household pollution from heating and cooking. The Integrated Exposure-Response (IER) model has now been extensively used to examine strategies to improve air quality throughout the world. But what is its future? Can the IER assumptions of equal toxicity among sources, independence of dosing rate, and exposure equivalence based on total inhaled dose even be clearly tested? Can it be replaced by simpler models requiring fewer assumptions? These issues will be explored in light of new information collected over the past decade.
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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 | 0.038 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".