IMAGING ELEMENT-DISTRIBUTION PATTERNS IN MINERALS BY LASER ABLATION - INDUCTIVELY COUPLED PLASMA - MASS SPECTROMETRY (LA-ICP-MS)
Bibliographic record
Abstract
We demonstrate the application potential of laser-ablation - inductively coupled plasma - mass spectrometry (LA-ICP-MS) to map the distribution of major and trace elements in a variety of samples. The examples cover a wide range of elements, including the rare-earth elements (REE) and platinum-group elements (PGE). In order to test the capabilities of the technique, samples of different matrices were analyzed (i.e., carbonates, silicates and sulfides). The main obstacle to rapid processing of element-distribution maps by laser ablation was data processing. This has been overcome with the development of new software, such as IOLITE, and improved designs of the laser-ablation cells and refinements of commercially available laser systems. It is possible to obtain fully quantified concentration maps for single-matrix samples using parallel adjoining line-scans. Single spot-analyses will result in better precision and accuracy, but the geochemical images are superior to conventional laser-ablation spot-analysis because they reveal geochemical details that are not visible under the microscope and cannot be appreciated with single spot-analyses. In addition to providing spatial information, the individual line-scans that are used in the image acquisition offer the option to obtain quantitative results along any part of the scan. Using LA-ICP-MS imaging, our dataset reveals zoned REE distribution in garnet crystals, a heterogeneous occurrence of the PGE in sulfides, as well as the internal chemical structures in ooids with respect to conditions of growth.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".