Improved in situ measurements of lead isotopes in silicate glasses by LA-MC-ICPMS using multiple ion counters
Bibliographic record
Abstract
A new technique that improves the spatial resolution and quantification limits of the measurement of lead isotope ratios in silicate glasses with <15 μg g−1 total Pb by LA-MC-ICPMS is presented. The new method provides the capability of making quantitative, in situ lead isotope measurements on tiny objects of geologic interest such as mineral growth bands, melt inclusions, and accessory minerals, even where they are lead poor. The method allows for the concurrent, static measurement of 204Pb, 206Pb, 207Pb, 208Pb along with 202Hg in five Channeltron ion counters. Standard-sample-standard bracketing using USGS BCR2-G as the calibrant is used to correct for instrumental mass bias. Accuracy and precision of the method was evaluated by replicate analyses of various MPI-DING reference glasses with low lead concentrations (∼1–11 μg g−1) and well-determined isotopic ratios. Spot sizes for in situ analyses were as small as 40–69 μm, providing better spatial resolution than previous LA-MC-ICPMS results reporting 204Pb. Measured lead isotope ratios for the MPI-DING reference glasses T1-G (11.6 μg g−1 total Pb) and ATHO-G (5.67 μg g−1 total Pb) agree within 0.10% and 0.15% respectively of the preferred values using 40 μm spots. For MPI-DING KL2-G (2.07 μg g−1 total Pb) and ML3B-G (1.38 μg g−1 total Pb) measured lead ratios agree within 0.75% of the accepted values with typical precisions of <1.9% (2RSD) using 69 μm spots; measured 207Pb/206Pb and 208Pb/206Pb are within 0.45% of preferred values with precisions of <0.50% (2RSD). These results demonstrate improvement over previous LA-MC-ICPMS data in terms of both quantification limits and spatial resolution, while retaining similar levels of accuracy and precision.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".