On the importance of grain size in luminescence dating using quartz
Bibliographic record
Abstract
There are two major problems commonly encountered when applying Optically Stimulated Luminescence (OSL) dating in the high dose range: (i) age discrepancy between different grain sizes, and (ii) age underestimation. A marked and systematic discrepancy between fine-grain (4–11 μm) and coarse-grain (63–90 μm) quartz single aliquot regeneration protocol (SAR) ages has been reported previously for Romanian and Serbian loess >40 ka (De of ∼100 Gy), generally with fine-grain ages underestimating the depositional age. In this paper, we show a similar age pattern for two grain size fractions from Chinese loess, thus pointing to a potential worldwide phenomenon. While age underestimation is often attributed to signal saturation problems, this is not the case for fine grain material, which saturates at higher doses than coarse grains, yet begins to underestimate true ages earlier. Here we examine the dose response curves of quartz from different sedimentary contexts around the world, using a range of grain sizes (diameters of 4–11 μm, 11–30 μm, 35–50 μm, 63–90 μm, 90–125 μm, 125–180 μm, and 180–250 μm). All dose response curves can be adequately described by a sum of two saturating exponential functions, whose saturation characteristics (D0 values) are clearly anticorrelated with grain diameter (φ) through an inverse square root relationship, D0 = A/√φ, where A is a scaling factor. While the mechanism behind this grain-size dependency of saturation characteristics still needs to be understood, our results show that the observation of an extended SAR laboratory dose response curve does not necessarily enable high doses to be recorded accurately, or provide a corresponding extended age range.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".