Improved dissolution and chemical separation methods for Lu‐Hf garnet chronometry
Bibliographic record
Abstract
Garnet‐based Lu‐Hf geochronology using conventional (HF‐HNO3) dissolution methods may be compromised by full or partial digestion of Hf‐rich zircon inclusions. This study integrates two complimentary methods to substantially reduce zircon digestion while assuring complete digestion of garnet and sample‐spike equilibration. Handpicked garnet fractions are heat treated to >1000°C in an evacuated silica glass ampoule to anneal zircon inclusions and then dissolved with 12 M HCl at 210°C and cold 28 M HF. Analyses of heat‐treated garnet (and their high‐temperature breakdown products orthopyroxene‐spinel‐quartz) from Gore Mountain, New York, demonstrate the method is capable of complete dissolution of garnet and routinely achieving sample‐spike equilibration. Independent analyses of annealed and unannealed zircons dissolved by HCl – cold HF demonstrate the benefit of heat treatment prior to dissolution. Analyses of zircon‐rich garnets from peletic paragneisses of Labrador, Canada, show the potential of this method by returning Lu‐Hf ratios commensurate with the degree of heat treatment. Finally, a complementary chemical separation method for Lu and Hf is presented that supports analysis of garnet by MC‐ICP‐MS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".