Testing the applicability of standardised growth curves (SGC) for chemically heterogeneous single-grain feldspars from the Atacama Desert, Chile
Bibliographic record
Abstract
The Atacama Desert is generally considered the driest non-polar desert on Earth and is therefore an ideal study area for exploring the water and biota free endmember of Earth’s Critical Zone (ECZ). Single grain (SG) luminescence dating has successfully identified processes in the ECZ. However, SG luminescence dating of Atacama Desert feldspars is challenging and time consuming since only a small fraction of grains emits sufficient luminescence and their potassium (K) contents, needed for internal dose rate calculations, are highly variable. Here we present an adaption of the standardised growth curve (SGC) method adjusted to the conditions of Atacama Desert sediments and a correlation of single-grain geochemistry and luminescence properties. To evaluate if SGCs are suitable for our study site and to determine the influence of the K-content on our luminescence age calculations, we used a set of five samples from the Atacama Desert and five chemically and structurally different feldspar sediment extracts from various geological origins worldwide. We performed a dose recovery test (DRT) using a post-infrared infrared stimulated luminescence (pIRIR) protocol and measured nine major element concentrations, including K, on a single grain level using a scanning electron microscope (SEM) with energy-dispersive X-ray spectroscopy (EDX). The DRT dataset was then used to test the application of SGCs. The accuracy of Atacama feldspar pIRIR measurements fitted onto SGCs frequently suffers from outliers in single measurement cycles. We investigate the influence of calculating a synthetic regenerative signal (sR) for SGC fitting, to reduce the effect of outliers on individual grain measurements. Furthermore, we reduced the regenerative cycles (rc) used for our sR approach, to test if shorter protocols would result in equivalent dose (De) estimates in agreement with longer protocols. We then calculated Spearman rank correlations between the results obtained with our modified SGC and the SAR protocol, luminescence signal intensities, and the geochemical dataset. Finally, we present a new method of fitting data onto a SGC which significantly decreases measurement time, without risking the inclusion of outliers. We furthermore show that the luminescence signal intensities, the De values and their dose recovery ratios obtained with our SGC method and a SAR protocol, are independent of the sample geochemistry.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".