Mass Bias Corrections for Hydrogen and Oxygen Isotope Analysis of Tourmaline by Secondary Ion Mass Spectrometry
Bibliographic record
Abstract
ABSTRACT Hydrogen (δ2H) and oxygen (δ18O) stable isotopes are used to trace fluid sources, metals, and contaminants in the environment and Earth's subsurface. Tourmaline-supergroup minerals provide an opportunity to quantify both δ2H and δ18O from the same grain using in situ analytical techniques (e.g., Secondary Ion Mass Spectrometry – SIMS). These minerals occur in a wide variety of geological environments and have a wide range of chemical compositions. However, large differences in chemical composition are problematic during SIMS analysis, as instrumental mass fractionation (IMF) often varies with the chemical composition of the mineral. Therefore, calibration models derived by analyzing tourmalines of different chemical composition must be developed for accurate analysis by SIMS. Hydrogen and oxygen isotope analysis was done on six reference tourmaline samples using a CAMECA 7f SIMS instrument operating at extreme energy filtering. Spot-to-spot repeatability for tourmalines was in the range 4–5‰ and 0.6–1.0‰ for δ2H and δ18O, respectively. There is a strong correlation between IMF and several elements (B, Si, Ca, Fe, and Fe#). Iron content is the most robust predictor of IMF, and we report two calibration curves for the correction of δ2H and δ18O measured by SIMS using reference tourmaline crystals with different Fe contents, ranging from 0.00 to 14.00 wt.% Fe. This is the first calibration curve used to correct for the fractionation of hydrogen isotope ratios in tourmaline as measured by SIMS. Tourmaline-supergroup minerals require a suite of at least three, with a range of Fe content, to ensure accurate and precise H and O analysis by SIMS. Crystallographic orientation effects were not observed for these tourmalines.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".