Forest Bathing: Do Forest Monoterpenes Affect Physiological Processes That Promote Health?
Bibliographic record
Abstract
This paper considers the health effects of ‘Forest Bathing’ and whether such health effects are mediated by forest volatile organic compounds (VOCs) and/or they’re whether these are through ‘mindfulness’ practices that activate health-promoting physiological processes. The VOCs considered are the monoterpenes D-limonene, α-pinene, ∆3 -carene and 1,8-cineole. Clearly, some of the health benefits of forest bathing come from ‘mindfulness’ practice that promote parasympathetic activities. There is also clear evidence that the 4 monoterpenes at high enough concentrations can activate physiological processes that lead to better health outcomes, particularly with decreases in anxiety and decreases in inflammation. These processes appear to involve activation of the adenosine A2A receptor and down-regulating activation of Nuclear Factor kappa B. The question is whether the monoterpenes can do so at the concentrations encountered in a forest setting. There is some research that indicates that forest atmospheric concentrations of monoterpenes can activate physiological processes that promote health but more research is required to definitively establish this. This review ends with suggestions on how to unequivocally answer the question whether the 4 monoterpenes considered at concentrations found in a forest, or their metabolites, can activate physiological processes that promote health, in particular physiological processes that decrease anxiety and inflammatory conditions and promote better endothelial function.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".