Bibliographic record
Abstract
Individual sounds and soundscapes can influence individuals 1 , their place evaluations 2 and potentially their psychological restoration 3 .As urban park soundscapes can vary greatly, ranging from quiet, serene oases to noisy city spaces, it is important to understand how they are perceived and evaluated, as they could influence people's experience and evaluation of the park in general.This paper studies the different types of soundscapes that are perceived in urban parks and examines if these soundscapes vary in their perceived restorativeness.Soundscapes can be described via a number of different methods, including measured acoustic and psychoacoustic parameters, or a professional's description of the foreground and background sounds.However, because of the different ways in which people listen (e.g.different 'listening types' 4 ), individual perceivers can notice different sounds and experience a different soundscape than one depicted by others.It is therefore important to identify the soundscape that is perceived by the individual who makes the soundscape evaluation.Methods for identifying people's perception of the soundscape include asking them to freely recall sounds 5 , or rate how often they heard a number of presented sound types 6 .Similarities in perceived soundscapes can be identified through free sorts of recorded soundscapes and examining the acoustic properties and sound sources of the subsequent categorisations 7,8. A combination of these methods was proposed to identify different categories of urban park soundscapes for this study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".