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Record W4206909492 · doi:10.1088/1361-6498/ac4e12

Estimation of population exposure to terrestrial gamma rays in Canada

2022· article· en· W4206909492 on OpenAlexaffabout
Jing Chen, Ken L. Ford

Bibliographic record

VenueJournal of Radiological Protection · 2022
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsGeological Survey of CanadaHealth Canada
Fundersnot available
KeywordsEnvironmental sciencePopulationOccupancyGeographyPhysical geographyRadionuclideEnvironmental healthEnvironmental protectionMedicineEcology

Abstract

fetched live from OpenAlex

Abstract Based on ground gamma ray spectrometry surveys conducted from 2007 to 2010 in populated areas across Canada (i.e. in southern Canada, excluding the northern territories), and with consideration of the exposure outdoors and indoors in various types of buildings as well as exposure to radionuclides in building materials (assuming most building materials are of local origin), the population-weighted annual effective dose from exposure to terrestrial gamma rays was estimated to be 167 ± 43 μSv. Under Canadian-specific average occupancy times, indoor exposures at home contribute 69% of the total annual effective dose, followed by 19% from indoor exposures other than at home, 6.2% from outdoor exposures and 5.8% from exposures inside vehicles. This assessment with measurements in a total of 1057 sites in populated areas across Canada is in general agreement with earlier assessments based on airborne gamma surveys mostly over unpopulated areas of Canada and truck-borne radiometric surveys along paved urban roads in four cities.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.349
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2022
Admission routes2
Has abstractyes

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