Methodology and Application of Hafnium Isotopes in Ilmenite and Rutile by <scp>MC</scp>‐<scp>ICP</scp>‐<scp>MS</scp>
Bibliographic record
Abstract
The high abundances of the high field‐strength elements in ilmenite and rutile make these minerals particularly suitable for hafnium isotopic investigations. We present a technique for separating Hf by ion exchange chemistry from high‐TiO2 (> 40% m/m) minerals to achieve precise Hf isotopic composition analyses by MC (multiple collector)‐ICP‐MS. Following digestion and conversion to chlorides, the first elution column is used to separate iron and the rare earth elements, the second column is designed to separate most of the titanium from Hf, an evaporation step using HClO4 is then performed to remove any trace of HF in preparation for the third column, which is needed to eliminate any remaining trace of titanium. The modified chemistry helped to improve the yields from < 10 to > 78% as well as the analytical precision of the processed samples (e.g., sample 2033‐A1, 176Hf/177Hf = 0.282251 ± 25 before vs. 0.282225 ± 6 after). The technique was tested on a case study in which the Hf isotopic ratios of ilmenite and rutile (analysed prior to the chemistry improvement) were determined and permitted to evaluate that the origin of rutile‐bearing ilmenite deposits is from the same or similar magma than their, respectively, associated Proterozoic anorthosite massifs (Saint‐Urbain and Lac Allard) of the Grenville Province in Québec, Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".