Improved dissolution and chemical separation methods for Lu‐Hf garnet chronometry
Bibliographic record
Abstract
Garnet‐based Lu‐Hf geochronology using conventional (HF‐HNO 3 ) 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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".