Bibliographic record
Abstract
• BACKGROUND AND AIMS Whole city studies, especially in cooler climates in advanced economies, have found profoundly large chronic health effects for those living short distances from large regional transportation related air pollution and noise sources, such as busy roadways, relative to similar populations living a kilometer or more from these exposures. (Oslo, Stockholm, Vancouver, Toronto, Boston, Copenhagen). While primary pollutants like ultrafine particles and noise have steep gradients next to large TRAP sources, akin to these health effects, regional pollutants like PM2.5 and ozone have flat or inverse gradients. The spatiotemporal granularity of research required to show these TRAP relationships is more challenging than regional studies keyed on PM2.5 or Ozone (Six Cities vs CAFEH). • METHODS We conducted a literature review of NLRP3 inflammasome and particulates. • RESULTS and CONCLUSIONS Both ambient particulate matter and NLRP3 inflammasome biology, specifically, have been shown to accelerate many major diseases of inflammation – sterile and pathogenic. Inflammasomes generate Interleukin 1 family cytokines such as IL-1beta. Of the 21 human inflammasomes, only NLRP3 reacts strongly to exogenous and endogenous particles. NLRP3 drives the health effects of a broad array of exogenous particles, including manufactured adjuvants, medical imaging agents, mineral fibres and ambient TRAP. And of endogenous particles such as crystalized uric acid, amyloid tau tangles, cholesterol and high molecular weight fatty acids. Environmental justice (EJ) populations tend to concentrate in locations with high TRAP exposures. Without more focused research into environmental justice exposures, that integrates spatiotemporal knowledge of TRAP with NLRP3 immunology, we will be unable to protect these EJ populations. NLRP3 inflammasome biology may be the missing link between TRAP and EJ populations. • KEYWORDS Near source exposures, transportation related air pollution, NLRP3 inflammasome biology, high exposure environmental justice populations
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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 | 0.000 | 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 | 0.001 | 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) | 0.002 | 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".