Impact of Traffic-Related Environmental Noise on School Children and Teachers in a Northeast Brazilian Capital
Bibliographic record
Abstract
Environmental noise in urban areas is known to cause functional abnormalities reflected in human health and behavior. In developing countries such as Brazil, public schools are exposed to high levels of noise due in part to poor infrastructure, especially with regard to noise protection. In schools exposed to noise levels above the limits specified by regulations and international guidelines, students and teachers are at greater risk of health problems, and performance may be compromised. In this study we evaluated the impact of environmental noise on children and teachers at three public schools (a kindergarten, an elementary school and a high school) in a Northeast Brazilian metropolis, with emphasis on noise generated by street, metro and air traffic. Environmental noise maps were generated with software and teachers were administered questionnaires focusing on the impact of noise pollution on health and performance. The study was intended to subsidize efforts at urban planning and public policy making by measuring actual noise levels and probing their possible effects. As shown by our results, public schools are in urgent need of noise protection measures, and enforcement of noise emission regulations for public transportation needs to be more emphatic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".