ESR dating of quartz revisited: towards a better understanding of ESR sensibilities through investigation of different quartz types and experimental reproduction of sediment cycling
Bibliographic record
Abstract
Quartz is one of the most common minerals in the Earth’s crust and can be found in a large diversity of geological environments. Due to its ubiquity and resilience towards weathering, it is a major mineralogical component in terrestrial sediments. A good understanding of quartz properties can thus be very useful to understand quartz dynamics and thereby Earth surface dynamic processes such as erosion, transport and deposition.Electron spin resonance (ESR) spectroscopy is a method which allows to characterize paramagnetic centres (unpaired electrons related to geochemical impurities) in quartz. Two paramagnetic centres generally present in quartz (Al and Ti-Li centres) are sensitive to ionizing radiation on the one hand and light exposure on the other hand and can be used to date the transport and deposition of quartz in the timescale of ca. 50 ka to 3 Ma making ESR dating of quartz an excellent tool to constrain quaternary geological processes.While this method has continuously been developed and improved over the last decades, some key aspects of the method, notably the sample dependency of dose response and sensitivity, remain poorly understood, leading to difficulties or even impossibility to date some samples.In this contribution, we aim to identify the influence of source-specific signature and sediment cycling on ESR dose response and sensitivity. Our research focuses on the well characterized Strengbach catchment in the Vosges mountains (NE France) which drains a large variety of quartz bearing rocks (granites, gneiss, sandstones). Quartz extracted from different bedrocks was analyzed geochemically by Laser ablation coupled to mass spectrometry (LA-ICPMS) and by ESR. Subsequently, the quartz was artificially irradiated and bleached in order to reproduce natural sediment cycling and reinvestigated by ESR. We discuss the observed ESR differences in relation with their geochemical signature and examine possible reasons for the observed results.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".