Coupled Si and O isotope measurements of meteoritic material by laser fluorination isotope ratio mass spectrometry
Bibliographic record
Abstract
laser system releasing silicon tetrafluoride and oxygen gasses. The oxygen gas is then purified to remove other reaction by-products through several liquid nitrogen traps before being captured onto a molecular sieve and transferred to an isotope ratio mass spectrometer. The silicon tetrafluoride gas is then purified using a supplementary line by repeatedly freezing to -196°C with liquid nitrogen and then thawing with an ethanol slurry at -110°C through a series of metal and Pyrex traps. The purified gas is then condensed into a Pyrex sample tube before it is transferred to an isotope ratio mass spectrometer for silicon isotope ratio measurements. This system has silicon yields of greater than 90% for pure quartz, olivine, and garnet standards and has a reproducibility of ±0.1‰ (2σ) for pure quartz for both oxygen and silicon isotope measurements. Meteoritic samples were also successfully analyzed to demonstrate this system's ability to measure the isotopic ratio composition of bulk powders with precision. This unique technique allows for the fluorination of planetary material without the need for wet chemistry. Though designed to analyze small aliquots of meteoritic material (1.5 to 3 mg), this approach can also be used to investigate refractory terrestrial samples where traditional fluorination is not suitable.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".