The Influence of Different Forest Landscapes on Physiological and Psychological Recovery
Bibliographic record
Abstract
Previous studies have reported that exposure to forest landscapes has many benefits on human physiological and psychological health, as well as effectiveness in reducing stress and improving mood depending on different types of landscape. This study examined the effects of different types of forest landscapes for indirect visual experiences on the physical and mental health of college students (N = 33). Three types of landscape images were selected, in which forest landscapes included vegetated landscapes and water features, and as a control, we set up images of urban landscapes without natural elements. Physiological and psychological assessment was performed before the experiment for each student, followed by each student being exposed consecutively to nine landscape images for 3 min (each type) and assessed after each exposure. The results showed that both forest landscapes decreased stress (p < 0.05 for all) and improved mood and self-esteem (p < 0.01 for all). In contrast, water landscapes showed a slightly higher impact on physical and mental health than vegetated landscapes, but there was no significant difference. Conversely, only for self-esteem, the response after viewing vegetated landscapes (VL, SD = 29.06 ± 3.38) was better than after water views (WL, SD = 28.21 ± 2.48). Despite significant differences between the two types of forest landscapes not being found in our findings, the benefits of forest landscapes were observed through understanding the health-promoting capacities of different forest landscapes.
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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 | 0.000 | 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 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".