A high throughput Rb-Sr dating method using solution tandem ICP-MS/MS (87Sr/86Sr) and standard addition calibration ICP-MS (Rb/Sr)
Bibliographic record
Abstract
Routine generation of large Rb-Sr age datasets has been limited by the time-consuming requirement of chromatographic isolation of Sr from Rb to avoid the isobaric interference of 87Rb+ on 87Sr+. Recently, direct measurement of interference-free 87Sr/86Sr has become possible with the advent of tandem quadrupole inductively coupled plasma mass spectrometry (ICP-MS/MS). Here, we describe a method that uses ICP-MS/MS to measure 87Sr/86Sr combined with a standard addition calibration for precise determination of Rb/Sr, allowing efficient generation of Rb-Sr ages. The method is demonstrated on a suite of ~50 to 90 Myrs (million years) old celadonite samples that are dated with age uncertainties typically of ±2 to 5 Myrs provided the sample Rb/Sr is >2. A method to separate the phyllosilicate fraction from the bulk sample, because it commonly has a higher Rb/Sr more suitable for dating, using differential settling is also described. Differential settling allows high Rb/Sr phyllosilicates to be separated from other phases ICP-MS/MS allows 87Sr/86Sr measurement without chromatographic isolation of Sr+ from Rb+ Single-spike standard addition ICP-MS was used for Rb/Sr measurement
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
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".