Perceived Occurrences of Soundscape Influencing Pleasantness in Urban Forests: A Comparison of Broad-Leaved and Coniferous Forests
Bibliographic record
Abstract
Perceived occurrences of soundscape reflect cognitive responses to perceived soundscapes. This research focuses on the relationship between perceived occurrences and pleasantness of soundscape in urban forests, and models these parameters. Soundscape information was gathered at 60 observation sites in urban forests, including perceived occurrences of soundscape (POS), pleasantness of perceived soundscape in urban forests (PSUF), and equivalent continuous A-weighted sound pressure level (LAeq). Twelve trained participants were exposed to the soundscape at each site for five minutes and filled out a questionnaire about POS and PSUF. The weight-ratio of perceived occurrences of soundscape (WPOS) was obtained from the POS. Pearson’s correlation coefficients and Stevens’ power law were conducted to test the applicability of the perceived occurrences of soundscape in psychophysical models. Results show that there is an interaction between the WPOS and PSUF in urban forests, and that psychophysical models are able to assess pleasantness of perceived soundscapes in urban forests. Findings show that pleasantness trends of geophony and biophony in broad-leaved forests and those in coniferous forests are opposite when the LAeq is increasing. Furthermore, by combining the WPOS, PSUF, and LAeq, the models were able to link the PSUF of geophony, biophony, anthrophony, and total soundscape in urban forests. Overall, results revealed that perceived occurrences of soundscape play a key role in linking the pleasantness of geophony, biophony, and anthrophony in urban forests.
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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 | 0.001 |
| 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) | 0.001 | 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".