Ultrafast, > 50 Hz <scp>LA</scp>‐<scp>ICP</scp>‐<scp>MS</scp> Spot Analysis Applied to U–Pb Dating of Zircon and other U‐Bearing Minerals
Bibliographic record
Abstract
LA ‐ ICP ‐ MS U–Pb detrital zircon studies typically analyse 50–200 grains per sample, with the consequent risk that minor but geologically important age components (e.g., the youngest detrital zircon population) are not detected, and higher abundance age components are misrepresented, rendering quantitative comparisons between samples impossible. This study undertook rapid U–Pb LA ‐ ICP ‐ MS analyses (8 s per 18–47 μm diameter spot including baseline and ablation) of zircon, apatite, rutile and titanite using an aerosol rapid introduction system ( ARIS ). As the ARIS resolves individual single pulses at fast sampling rates, spot analyses require a high repetition rate (> 50 Hz) so the signal does not return to baseline and mass sweep times (> 80 ms) that span several laser pulses (i.e., major undersampling of the signal). All rapid U–Pb spot analyses employed 250–300 pulses, repetition rates of 53–65 Hz (total ablation times of 4.1–5.7 s) and low fluence (1.75–2.5 J cm −2 ), resulting in pit depths of ca . 15 μm. Zircon, apatite, rutile and titanite reference material data yield an accuracy and precision (2 s ) of < 1% for pre‐Cenozoic reference materials and < 2% for younger reference materials. We present a detrital zircon data set from a Neoproterozoic tillite where > 1000 grains were analysed in < 3 h with a precision and accuracy comparable to conventional LA ‐ ICP ‐ MS analytical protocols, demonstrating the rapid acquisition of huge detrital data sets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".