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Record W4210437902 · doi:10.5658/wood.2018.46.6.735

Radon Exhalation from Five Wood Species

2018· article· en· W4210437902 on OpenAlexaboutno aff
Ju Yong Lee, Gyu Woong Choi, Seog Goo Kang

Bibliographic record

VenueJournal of the Korean Wood Science and Technology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsRadonExhalationEnvironmental scienceBiologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Radon radiation exposures in home have been posed as a potential cancer hazard. This research aims to present the basic data of the indoor radon concentration level by examining the radon exhalation rates of wood species. Radon exhalation rates from five commonly used wood species in Korean wood building construction were measured with Continuous Radon Monitor (CRM), Model 1028 (Sun Nuclear Co., USA) using the Closed Chamber Method (CCM). The mass exhalation rate was observed to vary from 0.00089 Bq·kg-1·h-1 to 0.00181 Bq·kg-1·h-1, whereas the surface exhalation rate was observed to be 0.00677 - 0.01517 Bq·m-2·h-1. The radon exhalation rate of Quercus accutissima Carruth (white oak) which has the highest density showed the highest figure among the five wood species, on the other hand, the rest of four species showed similar results which were similar to the radon exhalation rates of wood in the U.S.A. and Canada. The average of the concentration measured by the CCM represented well up to the second half-life period (7.7 days). Because result of these small quantities seems to indicate that radon exhalation from the tested wood species has almost negligible impact, the main culprit of the high indoor radon concentration is clearly derived from the background of surrounding wood house. Therefore, as a safety precaution, infrastructures made of wood materials should be designed with the consideration of influx of radon and built accordingly. Furthermore, it is highly desirable that wood will be needed to use for furniture and interior finishing material in indoor environment.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.347
Teacher spread0.299 · 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 designBench or experimental
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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

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