Measurement of Annual Effective Doses of Radon from Drinking Water and Dwellings by CR-39 Track Detectors in Kulachi City of Pakistan
Bibliographic record
Abstract
Radon concentration and annual effective doses were measured in drinking water and dwellings of Kulachi city of Pakistan. Twenty samples of drinking water were collected from various sources i.e. tap water, pond water, hand pump and tube well water. CR-39 (Columbia Resin-39) based NRPB (National Radiological Protection Board) radon dosimeters were used to measure the radon concentration. Among the various types of samples, the maximum average value of radon concentration was detected (1.218±0.005 Bq/L) in tube well water while the minimum average value was (0.602±0.003 Bq/L) in tap water. The annual effective dose was calculated from the measured radon concentration which varied from 4.39 × 10-3 to 8.89 × 10-3 mSv/y. The measured values of radon concentration as well as the annual effective dose were found within the United States Environmental Protection Agency (US-EPA) and World Health Organization (WHO) recommended limits.In order to carry out radon survey in dwelling, thirty CR-39 based NRPB dosimeters were installed in various buildings in the area under study. The maximum measured indoor radon concentration was found to be 270±22 Bq/m3 while the minimum was 21±2 Bq/ m3. The mean value of indoor radon concentration in bed rooms was 98 Bq/m3 which was within the International Commission on Radiological Protection (ICRP) recommended limits however, maximum concentration of 240 Bq/m3 was observed in a mud made room which was above the US-EPA and WHO new recommended limits. The mean annual effective dose from indoor radon was found to be 1.546 mSv/y which was within the ICRP recommended limits.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".