Rising Canadian and falling Swedish radon gas exposure as a consequence of 20th to 21st century residential build practices
Bibliographic record
Abstract
Abstract Radioactive radon gas inhalation is a major cause of lung cancer worldwide and is a consequence of the built environment. The average radon level of properties built in a given period (their ‘innate radon risk’) varies over time and by region, although the underlying reasons for these differences are unclear. To investigate this, we analyzed long term radon tests and buildings from 25,489 Canadian to 38,596 Swedish residential properties constructed after 1945. While Canadian and Swedish properties built from 1970 to 1980s are comparable (96–103 Bq/m 3 ), innate radon risks subsequently diverge, rising in Canada and falling in Sweden such that Canadian houses built in the 2010–2020s have 467% greater radon (131 Bq/m 3 ) versus Swedish equivalents (28 Bq/m 3 ). These trends are consistent across distinct building types, and regional subdivisions. The introduction of energy efficiency measures (such as heat recovery ventilation) within each nation’s build codes are independent of radon fluctuations over time. Deep learning-based models forecast that (without intervention) the average Canadian residential radon level will increase to 176 Bq/m 3 by 2050. Provisions in the 2010 Canada Build Code have not significantly reduced innate radon risks, highlighting the urgency of novel code interventions to achieve systemic radon reduction and cancer prevention in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".