Standoff mid-infrared reflectance spectroscopy using quantum cascade laser for mineral identification
Bibliographic record
Abstract
In this work, quantum cascade laser (QCL) mid-infrared (MIR) reflectance spectroscopy is used to discriminate silicate and carbonate minerals in a standoff measurement setting. The tunable external cavity QCL source that was used allows measurements from 5.2 μm to 13.4 μm wavelength, where the fundamental vibrational bands of silicates and carbonates are observed. Spectra measured from a half-core sample were analyzed using multivariate analysis to extract and identify the end-member spectra from the mixtures. The end-member spectra were compared and validated using the ASTER database spectra and the spectra measured on reference samples with the same QCL MIR reflectance spectroscopy setup. Spectra of minerals commonly found in the mining industry were compared: quartz, microcline, albite, chlorite, muscovite, biotite, calcite and dolomite. MIR reflectance spectroscopy using compact QCL sources allow rapid spectral measurements at standoff distances and high spatial resolution. All these advantages show the potential of QCL MIR reflectance spectroscopy for in-the-field mining applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".