Use of Zr for mass bias correction in strontium isotope ratio determinations using MC-ICP-MS
Bibliographic record
Abstract
Isotope abundance ratios and isotopic composition of strontium in a biological sample were determined using MC-ICP-MS whereby zirconium was admixed with solutions of digested NIST SRM 987 and samples and used for mass bias correction with implementation of a combination of standard-sample-standard bracketing and internal normalization. In this manner, the certified value of 8.37861 for 88Sr/86Sr in SRM 987 was used for mass bias correction of 90Zr/91Zr in two adjacent spiked solutions of SRM 987. Their average was then used to calculate mass bias corrected Sr isotope ratios in the sample. An approximate 2.5-fold improvement in precision of determination of 87Sr/86Sr and 88Sr/86Sr was obtained compared to that based on only the standard-sample-standard bracketing technique, although close matching of Sr and Zr concentrations is required in the standard and sample. Absolute isotope ratios of 0.0564240 ± 0.0000042, 0.709362 ± 0.000013 and 8.38034 ± 0.00010 (1SD) and δx/86Sr-values of −2.228 ± 0.075‰, −1.377 ± 0.018‰ and 0.207 ± 0.012‰ (1SD) for 84Sr/86Sr, 87Sr/86Sr, 88Sr/86Sr relative to SRM 987, respectively, were obtained characterizing a fish liver sample. In agreement with previous studies, evidence is presented for variation of 88Sr/86Sr in samples. Estimation of the measurement uncertainty confirmed that the major source of imprecision arises from the uncertainty in the certified value of 88Sr/86Sr in SRM 987 used for mass bias correction.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".