How Should Soundscape Optimization from Perceived Soundscape Elements in Urban Forests by the Riverside Be Performed?
Bibliographic record
Abstract
Urban forests by the riverside are important habitats for various animals and contribute various soundscapes for citizens. Unfortunately, urban forests are exposed to the influence of riverside traffic noises from freeways. This study aims to explore the spatial and temporal variation of soundscape, conduct soundscape optimization for multiple parameters, and find a balance and its interval of soundscape elements through optimizing a soundscape map. Questionnaires and measuring equipment were used to gather soundscape information in an urban forested area in Fuzhou, China. Diurnal variations and soundscape mapping were used to analyze spatial and psychophysical relationships between soundscape drivers. We then conducted optimization for a soundscape map, which included normalization, critical value determination, target interval of optimal SPL determination, and modification of SPL and mapping. Our findings suggest that biological activities and natural phenomena are potential drivers for diurnal variation of soundscapes, especially tidal phenomena contributing water and shipping soundscapes. Our results also suggest that all the high values of perceived soundscapes were found at the southwest corner of the study area, which includes both riverside and urban forest elements. Furthermore, we suggest combining both optimal soundscape and SPL correction maps to aid in sustainable design in urban forests. This can contribute to the understanding and methodology of soundscape map optimization in urban forests when proposing suitable design plans and conservation of territorial sound.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".