Accurate Determination of Sr Isotopic Compositions in Clinopyroxene and Silicate Glasses by <scp>LA</scp>‐<scp>MC</scp>‐<scp>ICP</scp>‐<scp>MS</scp>
Bibliographic record
Abstract
The low‐Sr content (generally < 100 μg g−1) in clinopyroxene from peridotite makes accurate Sr isotopic determination by LA‐MC‐ICP‐MS a challenge. The effects of adding N2 to the sample gas and using a guard electrode (GE) on instrumental sensitivity for Sr isotopic determination by LA‐MC‐ICP‐MS were investigated. Results revealed no significant sensitivity enhancement of Sr by adding N2 to the ICP. Although using a GE led to a two‐fold sensitivity enhancement, it significantly increased the yield of polyatomic ion interferences of Ca‐related ions and TiAr+ on Sr isotopes. Applying the method established in this work, 87Sr/86Sr ratios (Rb/Sr < 0.14) of natural clinopyroxene from mantle and silicate glasses were accurately measured with similar measurement repeatability (0.0009–0.00006, 2SE) to previous studies but using a smaller spot size of 120 μm and low‐to‐moderate Sr content (30–518 μg g−1). The measurement reproducibility was 0.0004 (2s, n = 33) for a sample with 100 μg g−1 Sr. Destruction of the crystal structure by sample fusion showed no effect on Sr isotopic determination. Synthesised glasses with major element compositions similar to natural clinopyroxene have the potential to be adopted as reference materials for Sr isotopic determination by LA‐MC‐ICP‐MS.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".