Simultaneous in situ determination of U-Th-Pb and Sm-Nd isotopes in monazite by laser ablation ICP-MS
Bibliographic record
Abstract
Results are presented for the in situ simultaneous determination of U-Pb and Sm-Nd isotopes in monazite using the Laser Ablation Split-Stream (LASS) method. This method uses a laser ablation system coupled to both a magnetic-sector inductively coupled plasma mass spectrometer (HR) (ICP-MS) for measuring U-Pb isotopes and multicollector (MC) ICP-MS for measuring Sm-Nd isotopes. The ablated material is split using a glass Y-connector and transported simultaneously to both mass spectrometers. In addition to Sm and Nd isotopes, the MC-ICP-MS is configured also acquire Ce, Nd, Sm, Gd, and Eu elemental abundances. This approach provides age, tracer isotope, and trace element data in the same ablation volume, thus reducing but not eliminating sampling problems associated with fine-scale zoning in accessory minerals. The precision and accuracy of the U-Pb method (along with the precision of the Sm-Nd method) is demonstrated by analysis of six well-characterized monazite reference materials. The LASS results agree within uncertainty with previously determined isotope dilution thermal ionization mass spectrometry (ID-TIMS) ages. Accuracy of the Sm-Nd method is assessed by comparing the LA-MC-ICP-MS results with ID-TIMS determinations on a well-characterized, in-house monazite reference material. The LASS method is then applied to monazite from the Birch Creek Pluton (BCP) in the White Mountains of southeastern California as a case study to illustrate the utility of this method for solving geologic problems. The U-Pb ages and Sm-Nd isotopic data determined using the LASS method support the conclusions drawn from previous results that monazite can record both timing and potential sources of hydrothermal fluids.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".