Particulate Matter (PM) Levels and Associated Health Risks at the Indonesian National Nuclear Energy Agency
Bibliographic record
Abstract
Air quality is one of the challenges to public health.Poor air quality is caused by the presence of air pollutants.WHO mentions particulate matter (PM) as one of the main pollutants.These pollutants have varied toxicity that can threaten public health.This study aims to measure PM pollutants.This study is an effort to monitor and improve air quality in the workplace.This study falls into the descriptive category, with a focus on detailing the levels of PM2.5 and PM10.The research design chosen was cross-sectional.The quantitative data collected shows the concentration of PM collected on filter paper.Sampling was carried out at six points (environmental health laboratory, radiochemistry laboratory, basement, sauna, facilities for Technologically Enhanced Natural Radioactive Material (TENORM) testing, and a parking lot) at the Indonesian National Nuclear Energy Agency by grab sampling.Air sample measurements were carried out using the direct method using the DustTrak DRX-8533 TSI tool with an MCE filter.The overall measurement results of PM concentrations exceeded the established quality standards.The highest concentrations of PM10 and PM2.5 were 18.24 mg/m 3 outdoors.This can occur due to anthropogenic activities such as various human, household, and machine activities.Exposure to PM can cause respiratory problems (clinical codification category J00-J06 and its derivatives).Several ways can be done such as cleaning the office workspace in the morning and evening using a wet mop or vacuum pump.Air quality in the workplace needs to be monitored to create a healthy work environment and health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".