Assessment of indoor environmental quality and seasonal well-being of students in a combined historic technical school building in Slovakia
Bibliographic record
Abstract
One of the major present challenges in the building sector is to construct sustainable and low-energy buildings with a healthy, safe, and comfortable environment. This study is designed to explore long-term impacts of indoor environmental quality (IEQ) parameters in a historic technical school building on the health and comfort of students. The main objective is to identify environmental problems in schools and to direct public policy towards the enhancement of in-service historic buildings. The collected data on five consecutive days in various seasons from five different classrooms indicate allergy in 45% and asthma in 10% of students. Environmental factors, such as temperature, draught, noise, or light, affected 51% of students’ attention. Low temperature, unpleasant air, noise, and draught were found to be the most frequent concerns for students. The lowest temperature was measured during spring at 17.6 °C, the lowest humidity of 21.1% in winter, the largest CO 2 amount in the air in autumn at 2041 ppm level, and the greatest total volatile organic compounds (TVOC) as 514 µg/m 3 . The experimental and statistical analysis results suggest the necessity of a comprehensive restoration of the building with a focus on enhancement of IEQ as well as replacement of old non-standard materials. An effective ventilation system is also necessary. The building requires major renovations to preserve its historic features while safeguarding the well-being and comfort of students and staff. Further research is needed on acoustics, lighting, and energy factors as well as the health effects of old building materials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".