A Method for Secondary Ion Mass Spectrometry Measurement of Lithium Isotopes in Garnet: The Utility of Glass Reference Materials
Bibliographic record
Abstract
We present a novel method for the measurement of lithium isotopes in garnet utilising glass reference materials and secondary ion mass spectrometry (SIMS). Measured lithium isotopic compositions of natural garnets are heterogeneous, making them unreliable reference materials for in situ determination. However, SIMS lithium isotope measurements of glasses derived from these natural garnets are isotopically identical to their parent garnets and more homogeneous, demonstrating that they can be used as reliable reference materials. To characterise the composition dependence of instrumental mass fractionation (IMF), oxide and silicate powders were used to synthesise custom‐made glass reference materials (CGRMs) with garnet‐equivalent compositions. Results for six CGRMs measured by SIMS show a significant linear relationship between IMF and FeO and MnO contents. Corrections for this compositional IMF result in changes of up to 12‰ within the compositional range explored. Uncertainty in IMF‐corrected SIMS analyses with a 20 μm spot is in the range of 2.5 to 4.5‰, depending on the garnet composition and reference materials used. The method for in situ lithium isotope measurement in garnet by SIMS presented here is highly adaptable, valid across a range of Al‐rich garnet compositions, and yields spatial resolution and precision necessary to address a range of geological applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".