Impact of noise pollution during the COVID-19 pandemic in a hospital area in Sorocaba city, São Paulo State, Brazil
Bibliographic record
Abstract
Environmental noise is a public health problem that arises mainly from vehicular traffic. In noise-sensitive areas, such as hospitals, the harm is even greater, as noise affects the recovery of patients and causes stress and disturbance to employees. Noise control measures are usually restricted to simulations and mathematical modeling. Given this context, the present study assesses environmental noise around a public hospital in Sorocaba city, São Paulo State, Brazil, before and during the COVID-19 pandemic, benefiting from measures to restrict the circulation of vehicles and people. Measurements were performed in triplicate, on weekdays, at four points around the hospital during the day, and followed the guidelines of standard NBR 10.151/2019. The number of light and heavy vehicles was counted manually. The equipment used was the BK 2260 analyzer and a tripod with adjustable height. The circulation of light and heavy vehicles decreased significantly during the pandemic. However, this decrease was not enough for sound levels to meet the 50 dB(A) recommended for noise-sensitive areas. This fact can be due to the speed of the remaining vehicles being above the established for the surrounding streets. Vehicles are the main responsible for the high levels of noise in the area, overlapping the levels generated by the different activities in the study site.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".