Determining major and trace element compositions of exposed melt inclusions in minerals using ToF‐SIMS
Bibliographic record
Abstract
Abstract Melt inclusions (MI), tiny portions of liquid silicate (melt) and gases trapped inside crystals during their growth in magmatic environments, are a valuable geological tool for understanding the evolution of magmas and the source of metals for some ore deposits. Owing to their small size, acquiring the chemical composition of MI is a meticulous process. Time‐of‐Flight Secondary Ion Mass Spectrometry (ToF‐SIMS) is a powerful surface analytical technique that provides excellent sensitivity with high mass and spatial resolution. We have begun the study of MI in rocks associated with modern and ancient seafloor hydrothermal systems. The main objective of our experiments is to obtain quantitative data for which we have used as standards quenched glasses with known similar compositions. Specimens were polished and different cleaning conditions (presputtering with either Ar or Cs ions) were examined in order to optimize our results, which were obtained using a Bi cluster ion source. Good quantitative results were obtained for selected major and trace elements (e.g. Mg, Na, K, V, La and Ce). Copyright © 2010 John Wiley & Sons, Ltd.
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 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 | 0.000 |
| 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) | 0.002 | 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".