Emergences of New Technology for Ultrafast Automated Mineral Phase Identification and Quantitative Analysis Using the CORIOSITY Laser-Induced Breakdown Spectroscopy (LIBS) System
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
Geochemical and mineralogical characterization studies play an important role in the definition of mineral deposits. Each mineral system has a unique set of minerals with different chemical makeup and physical properties. Platinum-group elements (PGEs), for example, are scarce resources with many applications. The optimization of extraction process efficiency is therefore crucial to prevent resource shortage and increased bulk prices. To improve the mineral liberation process, high throughput sensors must be added alongside the production line as part of fast process analysis implementation. Current analytical methods are either ineffective to assess PGE content, or unusable in the conditions of the processing facilities. This article shows how Laser-induced breakdown spectroscopy (LIBS) technology, developed by ELEMISSION Inc, can circumvent these drawbacks by enabling automated, ultra-fast, and precise quantitative mineral analyses in any working environment. The drill core samples that were used in this study were collected at the Stillwater platinum group element mine in the United States. The data used for the mineralogical database was validated using the TESCAN Integrated Mineral Analyzer (TIMA) instrument.
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 | 0.001 |
| 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) | 0.000 | 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