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Record W4405921348 · doi:10.3390/atmos16010021

Characterizing Radon Among Public Buildings and Small/Medium-Sized Businesses in a Canadian Province

2024· article· en· W4405921348 on OpenAlexaffabout
Tracy L Kirkham, Laura Boksman, Anne‐Marie Nicol, Paul A. Demers

Bibliographic record

VenueAtmosphere · 2024
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsSimon Fraser UniversityPublic Safety CanadaOccupational Cancer Research Centre
Fundersnot available
KeywordsRadonBusinessEnvironmental scienceMeteorologyAgricultural economicsArchitectural engineeringGeographyEngineeringEconomicsPhysics

Abstract

fetched live from OpenAlex

Radon is a naturally occurring radioactive gas that causes lung cancer. It has been measured extensively in homes and mines but research in other workplaces has been limited. The present study examined 453 workplaces in Ontario, Canada, to characterize radon levels. Radon monitors (n = 687) were placed in occupied ground floor and basement workplace locations for a minimum of three months. The radon measurements ranged from <4 to 566 Bq/m3, with a median of 26 Bq/m3, arithmetic mean of 40.2 Bq/m3, and geometric mean of 26.9 Bq/m3. Using the Health Canada and Ontario labor guideline of 200 Bq/m3, 2.5% of participating workplaces had at least one measurement above this level; 7.2% were above the World Health Organization guideline. Workplaces were also asked to fill out questionnaires to identify possible determinants of exposure. Radon levels varied significantly based on municipality and background radon zone, highlighting the importance of geography in influencing radon levels. Radon levels also varied significantly based on window-opening behavior, business access type, the presence of an elevator, air conditioning, additions to the building, and cracks and/or gaps in the foundation/wall and around drains, indicating building characteristics with some influence on air circulation may impact overall radon levels.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.482
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.315
Teacher spread0.270 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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