TIME-OF-FLIGHT SIMS (TOF-SIMS) ANALYSES OF MELT INCLUSIONS
Bibliographic record
Abstract
Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a valuable tool for analyzing very small geological materials, like exposed melt inclusions, because it provides high mass and spatial resolution with little sample destruction. In this study, ToF-SIMS quantitative analysis of major and trace elements and isotope ratios were determined in a vapor bubble wall of a melt inclusion and in its host clinopyroxene. Two standard glass reference materials, BCR-1 and JB-2, of similar composition were used. Results indicate that quantification using ToF-SIMS is possible for many mineral-forming elements. High mass and spatial resolution elemental maps clearly show the preferential partitioning of S, Na, Cu, Au, Li, and K into the vapor bubble in the clinopyroxene-hosted melt inclusion. Moreover, the presence of C, H, and associated hydrocarbon fragments in the element maps suggest the heterogeneous entrapment of brine and hydrocarbon metal-bearing phases in the melt inclusion. Variability in isotope ratios found particularly in the standard reference materials suggests either or both heterogeneous distribution in the sample or analytical variability. In any case, more research is necessary in order to better constrain the multitude of variables.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".