Assessment of palladium concentration in drill cores using laser-induced breakdown spectroscopy (LIBS)
Bibliographic record
Abstract
Laser-induced breakdown spectroscopy (LIBS) appears to be a promising technique for rapid on-site assessment of precious metal concentrations in ores. However, a number of issues need to be considered for the optimal use of this technique in practical situations. This article focuses on the number of measurements (i.e., spectra or laser shots) required to assess the mean palladium concentration in drill cores from the Lac des Iles mine (Ontario, Canada). We have performed a probabilistic study of the accuracy of the mean palladium concentration obtained by LIBS as a function of the number of measurements at random locations. For this purpose, we first used the results of a detailed laser scan of the core surface and then a mathematical model of the probability density of the palladium distribution to explore the parameter space, in particular the effect of noise on the measurements. We show that for the 1-meter-long, 2-centimeter radius quarter core samples analyzed, a few thousand randomly sampled locations generally provide an assessment of the palladium concentration within useful confidence limits. For a typical laser repetition rate of 50 Hz, such an analysis is a matter of minutes compared to hours or days using conventional methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".