Factors Influencing Natural and Cultural Soundscape Interactions on Perceptual Experiences in Forested–Historical Interface Areas
Bibliographic record
Abstract
The quality of the soundscape in historical districts is receiving increasing attention from urban governments due to its significant potential to highlight historical characteristics and enhance the acoustic environment of urban areas. However, there is still a lack of research on the relationship between natural and cultural soundscapes as they interact in historic areas. Using the historical area of Wuhou Shrine Museum in Chengdu as a case study, this study analyzed the differences in sound levels, sound source recognition, and subjective perception between two distinct spatial types: the historical street and adjacent urban forest. Additionally, structural equation modeling (SEM) was employed to explore the impact of sound source recognition and sound levels on subjective perception. The results reveal the following: (1) The soundscape interaction between the historical street and the adjacent urban forest exhibits a conflicting relationship, with cultural and natural soundscapes struggling to coexist harmoniously. (2) Within the historical region, L10 has the strongest effect on subjective evaluation, while L90 has the weakest. (3) Quietness is not always positively correlated with comfort and pleasure, indicating that a tranquil environment does not necessarily enhance pleasantness. These findings provide differentiated soundscape optimization strategies tailored to historical areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".