IMAGING ELEMENT-DISTRIBUTION PATTERNS IN MINERALS BY LASER ABLATION - INDUCTIVELY COUPLED PLASMA - MASS SPECTROMETRY (LA-ICP-MS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 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 it