Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
At an international level it is recognised that urban noise has serious and negative public health impacts. This leading editorial and the special issue it accompanies seeks to broaden this agenda. An important goal for Cities & Health is to give ear to new urban health topics, methods and collaborations. In doing so this paper presents the topic of urban sound and health from several unique angles. At its core, we deliberately move the focus beyond noise levels, as measured by decibels, and harm to health through the stress of relentless background noise. Instead, we focus on the concept of soundscape, a more qualitatively nuanced research subject of enquiry. The paper serves as an introduction to soundscape and health from several distinct disciplinary positions and lays a good intellectual foundation for the twenty-two papers published in this special issue. We hope that through a soundscape approach we can encourage fresh thinking about urban sound, including how people perceive and relate to their sonic environments, and show how sound can contribute to health. We believe that this approach can provide a collaborative platform for sound artists, sound technologists, urbanists and local people to work together with public health and create healthier urban environments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it