Evaluation of the Impact of Indoor and Outdoor Background Ionizing Radiation on Health risk in two Physics University Laboratories
Bibliographic record
Abstract
This study investigates the levels and health implications of indoor and outdoor background gamma radiation in two university physics laboratories in Sudan University of Scienece and Technology, Sudan.Given the constant exposure to ionizing radiation from natural and artificial sources, including building materials and radioactive teaching aids, the study aimed to quantify radiation exposure risks to staff, students, and visitors.Using a Geiger-Müller (GM) tube and digital counter, radiation levels were measured at various points in and around the laboratories.Results showed that average indoor radiation doses were consistently higher than outdoor levels in both laboratories, with Lab 1 recording an average indoor dose of 150.92 nSv/h compared to 114.73 nSv/h outdoors, and Lab 2 showing 81.91 nSv/h indoors versus 71.42 nSv/h outdoors which is less than the global radiation threshold.Although some indoor readings approached established high-dose thresholds, none significantly exceeded them.The data suggest that indoor sources, possibly building materials or equipment, contribute to elevated exposure, though not at levels requiring immediate intervention.These findings support the need for continued monitoring and the establishment of safety guidelines to mitigate long-term exposure risks in educational laboratory environments.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".