A comparison of consumer-grade electronic radon monitors
Bibliographic record
Abstract
Abstract Radon is a radioactive gas which is naturally occurring in soil and can accumulate to concentrated levels inside homes and buildings. Exposure to elevated levels of radon leads to an increased risk of developing lung cancer. In recent years there has been a rise in the popularity of consumer-grade electronic radon monitors. The monitors are appealing to homeowners due to the ease of use and the ability to keep track of radon levels during the process of conducting a radon test. However, there is currently no independent process to evaluate the relative performance of these monitors against known levels of radon. In this study, three sample units of six different models representing three different manufacturers of consumer-grade electronic radon monitors were exposed to three different levels of radon in a controlled environment to evaluate their precision and accuracy. Two separate tests were conducted at the Canadian guideline level to accommodate for ‘indoor winter’ and ‘summer’ conditions. The purpose of the study was to compare the performance of the different consumer-grade electronic radon monitors and determine which factors should be considered when using these monitors to inform mitigation decisions. The monitors had a range of uncertainty from 2%–15% with a range of precision from 1%–24%. The monitors performed better at higher radon levels than at levels near the Canadian guideline level of 200 Bq m −3 , and slightly better during ‘summer’ conditions than during ‘indoor winter’ conditions. While the monitors provide homeowners with a very specific number indicating their radon level, it was noted that this number should be considered with respect to a ‘confidence ratio’ or ‘range’ which could be done through a publicly available online tool which could provide the radon level range for a given radon level and device grade.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".