Verification of correction factors for determining mean annual levels of radon in underground facilities
Bibliographic record
Abstract
The first verification of a tool developed to improve the work of controlling bodies, managers and employees of underground facilities subject to radiation protection requirements was conducted. The recommended values of correction factors were verified using archival results of measurements conducted for the Institute of Occupational Medicine in Łódź in seven underground workplaces in Poland over exposure periods of a month (10,8678 data) and a quarter of a year (53,688 data). In a cave two groups of monthly factors, produced estimates with almost 70% to 99% consistency with the measured values. Along tourist routes located in mines, a similar fit was obtained using three groups of correction factors for measurement results from March, June and July. In the extraction areas of active underground mines, the best fit was produced by factors calculated as averages for spaces varying in the degree of insulation and ventilation method, while in other departments of mining plants, by correction factors recommended for facilities equipped with mechanical ventilation systems. All the quarterly correction factors produced the best fit between estimated mean annual concentrations and measurement results obtained in the second quarter of the calendar year. A wide variation in result consistency (from 20-30 to 65-80%) obtained for two underground tourist routes in the fourth quarter of the year demonstrates that it is best not to adopt results from this measurement period (October-December) for estimating mean annual radon concentration using the set of quarterly correction factors.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".