Advanced laser-induced breakdown spectroscopy (LIBS) sensor for gold mining
Bibliographic record
Abstract
There is a need in the mining industry for determining quickly and in the field the concentration of gold in mineral ore samples. Existent portable analyzers cannot determine the gold content at the ppm range. Hence, a portable LIBS (Laser-Induced Breakdown Spectroscopy) appears as a good candidate but developments are required to fulfill the needs of the gold mining industry. Developing a functional LIBS based analyzer involves several challenges to be addressed prior to its use in the field. To be of practical use, the analyzer has to probe a representative sampling of the surface of the mineral samples and has to tackle the matrix effect resulting from several mineralogical compositions of the samples. This paper presents the recent on-going work at the National Research Council Canada (NRC) and Laval University using LIBS for gold mining, from the determination of gold-bearing rock composition to direct detection of gold and system prototyping.
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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) | 0.065 | 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 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".