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Record W3175185142

Geological controls on radon concentration in surficial sediment in Whitehorse, Yukon

2021· article· en· W3175185142 on OpenAlexaboutno aff
Michael Kishchuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyRadonSedimentGeochemistryHydrology (agriculture)Earth scienceGeomorphologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Indoor concentrations of radon gas in excess of HealthCanada guidelines have been reported throughout the Whitehorse area, yet only cursory measurements have been made to determine the concentration of radon in undisturbed near-surface settings. Radon-222 is a carcinogenic gas produced by the decay of 226Raas part of the 238Udecay series. Exposure to radon and its radioactive daughters is the second-leading cause of lung cancer in Canada. Information about its occurrence is therefore important for public healthand for policies such as building codes. Low concentrations of uranium in bedrock underlying the Whitehorse region suggest that surficial sediment may be a primary local source of 222Rn.To evaluate radon sources and activity, 30 sites representing a range of bedrock and surficial sediment types were evaluated in the summer of 2020. The underlying bedrock lithologies are granodiorite, limestone, clastic sediments, and basalt. The surficial sediment types include lodgement till, glaciofluvial sand and gravel, glaciolacustrine fine sand and silt, fluvialsand and gravel, and eolian sand. To determine controlling factors, mean radon concentration at each site was compared to bedrock lithology, surficial sediment composition, type, and thickness, grain size distribution, sediment maturity, soil moisture, matrix geochemistry, and clast geochemistry.A positive correlation was observed between grain size distribution and radon concentration, with sediments containing more silt and clay in their matrix displaying higher radon concentration, and this may be due to the decreased permeability of clay-rich sediments. Radon concentration is generally higher in less mature sediments (e.g. till) compared to more mature sediments (e.g. fluvial and eolian sand), suggesting that less weathered sediment types may produce more radon. No significant correlationwas observedbetween radon concentration and bedrock lithology nor depth to bedrock, possibly because sediment thickness in the Whitehorse region exceeds the distance radon can travel before it decays. Pronouncedinterseasonal variation was observed at three long-term monitoring sites, with little intraseasonal variation over the summer. This variation may be caused by seasonal freezing and thawing of the ground, an important consideration in northern landscapes. Geochemical analysis suggests that while some radon may be produced in near-surface settings, the controls examined in this study are primarily controls on transport.Sediment maturity and grain size are first order controlling factors of radon concentration insurficial sediment and bedrock is neither the only nor the most important source of radon in the study area. Key words: radon, glaciated landscapes, grain size distribution, sediment maturity, seasonal variation Pages: 77 Supervisor: John Gosse

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)0.000
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 score0.577
Threshold uncertainty score0.840

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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

Citations0
Published2021
Admission routes1
Has abstractno

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