Assessment of Time‐Gated Raman Spectroscopy for Online Mineralogy of a Spodumene Ore
Bibliographic record
Abstract
ABSTRACT This paper investigates the applicability of time‐gated Raman spectroscopy (TRS) for online mineralogy identification, focusing on a Li‐bearing spodumene ore. The study provides an overview of existing online mineralogy tools and points out some limitations of elemental analysis techniques. Dense media separation concentrate and tailing samples used originated from a deposit in Quebec, Canada. Various techniques, including ICP‐AES, XRD, laser diffraction, and specific gravity determination, were used for detailed sample characterization. Samples and their mixtures at known ratios were prepared as powder and slurries, before being analyzed using TRS, followed by spectrum quantitative analysis. Results show a linear correlation between spodumene percentage by weight and the main Raman peak height or area, in both dry and slurry form, with a higher determination coefficient in slurry form. TRS also provided indications as to the gangue minerals present in the samples. This study shows the potential for TRS as a tool for online mineralogical assessment of spodumene ores, and further work should target the determination of detection limits and quantification of both valuable and gangue minerals.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".