Younger North Americans are exposed to more radon gas due to occupancy biases within the residential built environment
Bibliographic record
Abstract
Abstract Residential buildings can concentrate radioactive radon gas, exposing occupants to particle radiation that increases lung cancer risk. This has worsened over time in North America, with newer residences containing greater radon. Using data from 18,971 Canadian households, we calculated annual particle radiation dose rates due to long term residential radon exposure, and examined this as a function of occupant demographics. The current particle radiation dose rate to lungs from residential radon in Canada is 4.08 mSv/y from 108.2 Bq/m 3 , with 23.4% receiving 100–2655 mSv doses that are known to elevate human cancer risk. Notably, residences built in the twenty-first century are occupied by significantly younger people experiencing greater radiation dose rates from radon (mean age of 46 at 5.01 mSv/y), relative to older groups more likely to occupy twentieth century-built properties (mean age of 53 at 3.45–4.22 mSv/y). Newer, higher radon-containing properties are also more likely to have minors, pregnant women and an overall higher number of occupants living there full time. As younger age-of-exposure to radon equates to greater lifetime lung cancer risk, these data reveal a worst case scenario of exposure bias. This is of concern as, if it continues, it forecasts serious future increases in radon-induced lung cancer in younger people.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".