Collocation testing of consumer-grade electronic radon sensors in Canadian homes
Bibliographic record
Abstract
Radon exposure is the second leading cause of lung cancer in Canada. Consumer-grade electronic radon sensors, which provide real-time results, are increasingly used by homeowners for radon monitoring. This study evaluates the short-term performance of four consumer-grade radon sensors (RadonEye, Inkbird, Spirit, and Wave) against a professional-grade instrument (RAD7) in seven different Canadian homes. In each home, testing was conducted for seven days with radon concentrations ranging from negligible to more than 2000 Bq/m 3 . Sensor performance was assessed using a total of six analysis methods, including linear regression, RMSE, Pearson or Spearman correlation analyses, Welch’s t-test or Mann-Whitney U-test, Bland-Altman plots, and confusion matrices. The results reveal that three of the four sensors had similar performance across most analysis methods, while one sensor had the lowest performance, possibly attributed to the short testing duration. For occupants, these sensors can be useful for quickly detecting current radon levels; however, long-term testing (90 days) with certified passive detectors is still recommended to ensure a reliable assessment of their radon exposure.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".