Determination of the Isotopic Composition of Iridium Using Multicollector-ICPMS
Bibliographic record
Abstract
Like many other elements, iridium is lacking a calibrated, SI traceable isotope ratio measurement. In this study, we have undertaken absolute isotope amount ratio measurements of iridium by multicollector inductively coupled plasma mass spectrometry (MC-ICPMS) using a state-of-the-art regression model to correct for the instrumental fractionation (mass bias) of isotope ratios using both NIST SRM 997 isotopic thallium and NIST SRM 989 isotopic rhenium as primary calibrators. The optimized regression mass bias correction model is based on incrementally increasing plasma power and short (10−30 min) measurement sessions. This experimental design allows fast implementation of the regression method which would normally require hours-long measurement sessions when executed under constant plasma power. Measurements of four commercial iridium materials provide a calibrated iridium isotope ratio R193/191 = 1.6866(6)k=1 which corresponds to isotopic abundance x191 = 0.372 21(8)k=1 and an atomic weight of Ar(Ir) = 192.217 63(17)k=1. In addition, we present data on a new Certified Reference Material from NRC Canada IRIS-1 which fulfills the requirements of a delta zero reference for iridium isotope ratio measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".