Evaluating Human Exposure to Fine Particulate Matter Part I: Measurements
Bibliographic record
Abstract
Abstract Exposure to airborne fine particulate matter has been a pressing issue since the early 1990s when several studies reported health effects at unexpectedly low ambient levels. Since this time, several reviews have addressed various aspects of this topic. This article is the first of a two‐part review of reviews. The intention of these articles is to provide a consolidated overview about fine particulate matter exposure assessment. This article, Part I, begins with a general introduction to particulate matter which includes general properties of particulate matter, how it is classified and how it is associated with health effects. Fundamental concepts related to exposure are also summarized. The remainder of the article focuses on measurement‐based methods for assessing exposure to fine particulate matter. A subsequent article, Part II, addresses modeling approaches used for particulate matter exposure assessment. Current and recommended future directions for assessing exposure to fine particulate matter are also summarized in each of these two articles.
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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 | 0.001 | 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) | 0.010 | 0.003 |
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".