Bibliographic record
Abstract
In addition to established effects on cardiovascular and respiratory systems, recent epidemiological studies show associations between air pollutants and impacts on the central nervous system such as depression and impaired cognitive ability, and on disease states associated with dysfunctional metabolism such as metabolic syndrome and type II diabetes. Although the relative risk attributed to air pollutants is small compared with established risk factors, the widespread exposure of the population translates into a substantial societal health burden. Controlled experimental studies support the notion that these associations have a biological basis. Recently, we reported that short-term exposure of rats to two pollutants associated with adverse health effects, particulate matter and ozone, activated the hypothalamic-pituitary-adrenal (HPA) stress response axis, resulting in increased circulating levels of the glucocorticoid corticosterone and systemic impacts on a variety of biological pathways. While effects were transient after a single exposure in this healthy animal model, chronic activation and dysfunction of the HPA axis is associated with adverse neurobehavioral, metabolic, cardiovascular, immune, reproductive, and developmental effects. Here I build upon the ideas presented in the original paper to discuss the potential role that activation and dysfunction of the stress axis, a common feature of neurobehavioral and metabolic disorders, may play in mediating adverse health effects associated with air pollution.
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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 | 0.001 | 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".