Accurate Hf isotope determinations of complex zircons using the “laser ablation split stream” method
Bibliographic record
Abstract
The “laser ablation split stream” (LASS) technique is a powerful tool for mineral‐scale isotope analyses and in particular, for concurrent determination of age and Hf isotope composition of zircon. Because LASS utilizes two independent mass spectrometers, a large range of masses can be measured during a single ablation, and thus, the same sample volume can be analyzed for multiple geochemical systems. This paper describes a simple analytical setup using a laser ablation system coupled to a single‐collector (for U‐Pb age determination) and a multicollector (for Hf isotope analyses) inductively coupled plasma mass spectrometer (MC‐ICPMS). The ability of the LASS for concurrent Hf + age technique to extract meaningful Hf isotope compositions in isotopically zoned zircon is demonstrated using zircons from two Proterozoic gneisses from northern Idaho, USA. These samples illustrate the potential problems associated with inadvertently sampling multiple age and Hf components in zircons, as well as the potential of LASS to recover meaningful Hf isotope compositions. We suggest that such inadvertent sampling of differing age and Hf components can be a significant cause of excess scatter in Hf isotope analyses and demonstrate that the LASS approach offers a robust solution to these issues. The veracity of the approach is demonstrated by accurate analyses of 10 reference zircons with well‐characterized age and Hf isotopic composition, using laser spot diameters of 30 and 40 µm. In order to expand the database of high‐precision Lu‐Hf isotope analyses of reference zircons, we present 27 new isotope dilution‐MC‐ICPMS Lu‐Hf isotope measurements of five U‐Pb zircon standards: FC1, Temora, R33, QGNG, and 91500.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".