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Record W4416396661 · doi:10.1017/qua.2025.10035

The impact of ionising radiation from geophysical loggers and water content on the luminescence signals in sediment cores

2025· article· en· W4416396661 on OpenAlexafffund
Sam Woor, Alex Hughes, Mitch D’Arcy, Olav B. Lian, Cooper Stacey, Randolph J. Enkin

Bibliographic record

VenueQuaternary Research · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsGeological Survey of CanadaUniversity of the Fraser ValleyUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsLuminescenceSedimentOptically stimulated luminescenceIonizing radiationThermoluminescence datingRadiation

Abstract

fetched live from OpenAlex

Abstract Luminescence dating and profiling are important analytical methods for providing chronological constraints and reconstructing depositional histories from sediment cores. However, sediment cores have often been exposed to ionising radiation sources during geophysical analyses, which potentially contaminates natural luminescence signals and may compromise the accuracy and reliability of luminescence analyses. Variable water content down-core is another potential issue for the rapid analysis of sediments, as water attenuates luminescence and may limit the comparability of samples. Here, we use a portable optically stimulated luminescence reader to test the influence of two common geophysical analyses—X-radiography and gamma-ray logging—on the luminescence properties of sediments in marine cores. We demonstrate that both techniques cause negligible changes to luminescence signals with doses <100 mGy. We test the effect of variable water content on luminescence and show that net signals are reduced by up to 70% at 30% moisture, relative to dry sediments. Accurate and reliable luminescence signals can be obtained from sediment cores despite prior exposure to ionising radiation from geophysical loggers or variable water content. However, the accuracy of luminescence measurements does require taking appropriate steps before analysis, like assessing the doses given by geophysical instruments at specific laboratories or drying samples.

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)0.000
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 score0.037
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.350
Teacher spread0.269 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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