LIBS mineralogy: quantitative mineralogy on the belt
Bibliographic record
Abstract
Quantitative mineralogy is an established discipline in the geosciences and is aimed at providing mineral grade and texture information for geological and mineral processing applications. The two core technologies underpinning Quantitative Mineralogical Analysis (QMA) are Scanning Electron Microscopy (SEM) in combination with Energy-dispersive X-ray Spectroscopy (EDS). Technologies such as QEMSCAN and MLA are now routinely used to optimise the performance of large-scale mineral processing plants in the base and precious metal sectors. One of the major disadvantages of the QMA methods used today is extensive sample preparation requirements which make application of this technique to real-time mineralogical characterization not feasible. A breakthrough has been achieved by the National Research Council Canada in collaboration with CRC ORE by developing a novel Laser Induced Breakdown Spectroscopy (LIBS) based technology capable of real-time mineralogical characterization of process streams without sample preparation. The potential applications of this technology include, but not limited to, in-pit muck piles, underground draw points, cross-belt analysis as well as slurries. This paper describes the development of the LIBS-based technology from a proof-of-concept (TRL2) to the construction of a prototype sensor and its validation in a simulated environment (TRL5). Future work is being planned to test and further validate the LIBS sensor on a mine-site which will progress the technology to its next readiness level TRL6).
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 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) | 0.001 | 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 teacher head, 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".