Technical note: Investigation into the relationship between zircon structural damage and Pb mobility using chemical abrasion, SIMS, Raman spectroscopy, and atom probe tomography
Bibliographic record
Abstract
Abstract. Chemical abrasion (CA), a two-step process of annealing and partial dissolution, is routinely applied to zircon grains prior to U–Pb geochronology to dissolve portions of the grains affected by Pb loss prior to analysis. Despite the utility of the technique, it is not clear what the more HF-soluble material produced in the annealing step is, what degree of lattice damage causes it to form instead of zircon, how to predict if a specific sub-volume of a zircon will survive CA, or how any of these processes relate to Pb mobility. In this study, we use secondary ion mass spectrometry (SIMS), Raman spectroscopy, and atom probe tomography (APT) to constrain what happens to both concordant and discordant zircon during each step of the CA process. We find that zircon in SIMS sputter craters which have undergone Pb loss generally have more heterogeneous Raman band widths than in those sputter craters where Pb has been retained. Annealing drastically reduces Raman band widths, but some heterogeneity is still present in discordant sputter craters. APT results from all samples which successfully ran were homogeneous in U, Pb, Th, and most other elements in all cases. This makes it hard to link Pb loss and lattice damage at the submicrometre scale by direct imaging in this study. However, as the zircon sputter craters with Pb loss show homogeneous APT results, we recommend against using homogeneous APT results as an indicator of closed-system U–Pb behaviour.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".