The neurophysiology of noise and health: Established and emerging neural networks
Bibliographic record
Abstract
There is strong evidence for the increased risk of ischemic heart disease from excessive noise, but evidence also supports effects on other health outcomes such as stroke and diabetes and more generally suggests that excessive noise exposures can interfere with the homeostatic function of several physiological systems (e.g., cardiovascular, metabolic, and immune). This exposure stress-response mechanism is commonly dichotomized as direct through sleep disturbance or indirect through some form of cognitive processing due to disturbance or annoyance. In either case, chronic activation of the hypothalamic-pituitary-adrenal stress axis provides a mechanistic link between exposure and systemic stress, but there is a limited understanding of how other structures in the limbic system are involved, and consequently a limited understanding of how noise is processed and appraised among other sensory inputs and as a subjective experience. This is furthermore limited by exposure estimation methods and defining the acoustic and built environments that people experience. This paper provides a review of systemic stress activation in the context of emerging knowledge on sensory processing within the limbic system and highlights recent research on effects of sound and noise on mental health and cognition that are advancing our understanding of noise effects on health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".