The impact of ionising radiation from geophysical loggers and water content on the luminescence signals in sediment cores
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".