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Record W2335116850 · doi:10.1093/rpd/ncr396

Assessment of test duration effect in indoor radon measurement by Monte Carlo simulations

2011· article· en· W2335116850 on OpenAlexaffabout
J. Chen, D. Moir

Bibliographic record

VenueRadiation Protection Dosimetry · 2011
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsHealth Canada
Fundersnot available
KeywordsRadonMonte Carlo methodEnvironmental scienceStatisticsNuclear medicineMedicineMathematicsPhysicsNuclear physics

Abstract

fetched live from OpenAlex

To better understand the effect of various test durations on indoor radon measurement results in Canada, Monte Carlo simulations were performed for test durations of 1 month (30 d), 2 months (61 d), 3 months (91 d) and 6 months (183 d). For each of the specified test durations, a total of 1500 Monte Carlo simulations were performed. Each simulation was compared with the result of a 1-y measurement. On average, the radon concentration estimated from a 30-d test differed by about ±22 % from the value of a 1-y measurement. The difference reduced to about ±17 % for a 61-d test, ±14 % for a 91-d test and ±9 % for a half-year test. Health Canada's recommendation of a 3-month radon test performed during the heating season resulted in an estimated radon concentration, on average, ∼20 % higher than the value determined from a 1-y measurement. This ensures a conservative estimate of the annual average radon concentration, as there is some risk at any radon level. Therefore, to avoid an underestimation of radon exposure and to ensure appropriate levels of precision and accuracy are met, the results from this study suggest that a radon measurement duration of 3 months or longer during the heating season (from October through to April) is needed.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.366
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2011
Admission routes2
Has abstractyes

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