Combined Separation of Cu, Fe and Zn from Rock Matrices and Improved Analytical Protocols for Stable Isotope Determination
Bibliographic record
Abstract
Isotope ratios of heavy elements vary on the 1/10000 level in high temperature materials, providing a fingerprint of the processes behind their origin. Ensuring that the measured isotope ratio is precise and accurate depends on employing an efficient chemical purification technique and optimised analytical protocols. Exploiting the disparate speciation of Cu, Fe and Zn in HCl and HNO3, an anion exchange chromatography procedure using AG1‐×8 (200–400 mesh) and 0.4 × 7 cm Teflon columns was developed to separate them from each other and matrix elements in felsic rocks, basalts, peridotites and meteorites. It required only one pass through the resin to produce a quantitative and pure isolate, minimising preparation time, reagent consumption and total analytical blanks. A ThermoFinnigan Neptune Plus MC‐ICP‐MS with calibrator‐sample bracketing and an external element spike was used to correct for mass bias. Nickel was the external element in Cu and Fe measurements, while Cu corrected Zn isotopes. These corrections were made assuming that the mass bias for the spike and analyte element was identical, and it is shown that this did not introduce any artificial bias. Measurement reproducibilities were ± 0.03‰, ± 0.04‰ and ± 0.06‰ (2s) for δ57Fe, δ65Cu and δ66Zn, respectively.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".