Laser-induced Breakdown Spectroscopy (LIBS) in the Field: How Rock Moisture Influences Spectral Quality and Plasma Properties
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Bibliographic record
Abstract
Before LIBS can be applied to analysis at mine sites under normal weather conditions, a number of practical issues need to be addressed.One of these is the moisture content of rock samples taken directly in the field.To assess the effect of rock moisture content on LIBS measurements, we studied its temporal evolution as the rock dried under ambient laboratory conditions using a series of 1080 laser shots (18 rows 60 columns) at 2 Hz (8 ns pulse duration, 1064 nm wavelength, with a fluence of ~4 kJ cm -2 ).At maximum moisture, the LIBS spectra are weak, with only a few strong lines emerging from the background noise, while a richer spectrum appears as the rock dries.Color maps of LIBS spectra averaged over wavelength (white light) from the rock surface and net H line intensity from the water layer form complex, weakly correlated mosaics whose components depend on local rock properties (e.g., composition, porosity, asperities).However, the time evolution of their average over each of the 18 rows correlates well with that of the rock weight and microwave moisture measurements.Using the H line broadening and the ratio between the Mg II 280.27 nm and Mg I 285.21 nm line, the space and time averaged plasma produced in both the dry and wet rock areas is characterized by an electron number density in the range of 10 17 cm -3 and an ionization temperature close to 1 eV.The physical mechanisms involved are discussed.This study highlights the importance of controlling the moisture of the rock at the mining site before starting LIBS measurements, as it has a significant impact on the accuracy of the results obtained.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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