Particulate Matter (PM) Levels and Associated Health Risks at the Indonesian National Nuclear Energy Agency
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
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.
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 | 0.000 |
| 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