The Potential of Reflectance and Laser Induced Luminescence Spectroscopy for Near-Field Rare Earth Element Detection in Mineral Exploration
Bibliographic record
Abstract
New energy, transport, computer and telecommunication technologies require an increasing supply of rare earth elements (REEs). As a consequence, adequate and robust detection methods become essential for the exploration and discovery of new deposits, the improved characterization of existing deposits and the future recycling of today’s high-tech products. Within this paper, we investigate the potential of combining passive reflectance (imaging and point sampling) with laser stimulated luminescence (point sampling) spectroscopic measurements across the visible, near and shortwave infrared for REE detection in non-invasive near-field mineral exploration. We analyse natural REE-bearing mineral samples from main REE-deposits around the world and focus on challenges such as the discrimination of overlapping spectroscopic features and the influence of the mineral type on detectability, feature position and mineral matrix luminescence. We demonstrate that the cross-validation of results from both methods increases the robustness and sensitivity, provides the potential for semi-quantification and enables the time- and cost-efficient detection of economically important REE, including Ce, Pr, Nd, Sm, Eu, Dy, Er, Yb and potentially also Ho and Tm.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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 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".