Radon control systems in existing and new construction: a review
Bibliographic record
Abstract
In support of the implementation of the new Canadian radon guideline, a comprehensive review of radon mitigation techniques used in countries around the world was undertaken, with particular emphasis on North America and Europe that have climates and construction techniques similar to Canada. The results of this review are presented here as an aid to administrators of radon control programmes, companies offering radon testing and mitigation services and other concerned parties, both in Canada and elsewhere, who are facing issues of implementing a radon control strategy. A wide variety of radon mitigation strategies have been employed worldwide and all have achieved some success in reducing radon concentrations. Generally, active mitigation techniques involving physical alterations to a house (e.g. sub-slab depressurisation) are more effective in achieving a sustained and substantial radon reduction than passive techniques (e.g. improved ventilation or sealing of cracks). To a large extent, the choice of an optimal mitigation strategy will depend on the building type, soil conditions and climate. Radon levels should be measured at periodic intervals after remediation, perhaps once every 5 y, to ensure that concentrations continue to remain at acceptable levels.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".