The Effects of Soundscape Interactions on the Restorative Potential of Urban Green Spaces
Bibliographic record
Abstract
From the perspective of landscape environment and human health, this study introduces the concept of soundscape from soundscape ecology. Through two experiments evaluating the restorative properties of soundscapes, it analyzes and compares the differences in restorative benefits among various sounds in urban green spaces. The study further explores the effects of single soundscapes and combined soundscape types on environmental restorative benefits and provides recommendations for creating restorative soundscapes in urban green spaces. The main findings of this study are as follows: (1) Sound types significantly influence soundscape restorative benefits, with notable interactions observed among three single soundscape categories. Significant differences were also found in the restorative effects of different combined soundscapes. (2) The most restorative sounds for anthropogenic, biophonic, and geophonic soundscapes are light background music (1.4193), bird sounds (1.9890), and flowing water sounds (1.2691), respectively. The least restorative sounds are vehicle noise (−2.6210), conversation sounds (−0.8788), and thunder sounds (−0.7840). (3) Significant differences exist between the restorative effects of single and multi-level combined soundscapes. Except for bird sounds, the general restorative pattern is as follows: two-level combined soundscapes > three-level combined soundscapes > single soundscapes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".