Assessment of indoor exposure to outdoor environmental noise and effects on occupant comfort in multi-unit residential buildings
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
Outdoor environmental noise is a major source of annoyance in urban areas and exposure to it can increase the risk of severe health issues. Consequently, it has been the focus of research for decades. Even though people spend the majority of their time indoors, most studies use outdoor noise levels and do not include indoor noise measurements to estimate real exposure levels. This study conducted simultaneous indoor and outdoor noise measurements for 24 h in four multi-unit residential buildings to identify the levels and sources of outdoor noise heard indoors and quantify the effects of outdoor noise on indoor levels. The measurements were conducted in unoccupied suites that are most exposed to traffic and other outdoor noise sources. Surveys were administered following building occupancy to collect information regarding perceived acoustic comfort levels due to outdoor noise. The indoor L Aeq,24h in three of the study buildings were above 40 dB(A) and exceeded WHO’s noise level limits. Regression analysis showed that outdoor noise only explains 14%–58% of the variability in indoor noise levels. This is mainly because of heating, ventilation, and air conditioning (HVAC) system noise which resulted in consistently high indoor noise levels despite variations in outdoor noise. Analysis of the survey showed a poor correlation between reported annoyance and measured noise levels. But annoyance strongly depended on other factors such as suite location and noise sensitivity. The findings show that outdoor noise measurements alone may not be good predictors of exposure levels and the effects of outdoor noise on occupants.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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