Calibrating a Handheld LIBS for Li Exploration in the Barroso–Alvão Aplite-Pegmatite Field, Northern Portugal: Textural Precautions and Procedures When Analyzing Spodumene and Petalite
Bibliographic record
Abstract
In pegmatites containing abundant petalite and spodumene, such as those from the Barroso–Alvão (BA) aplite-pegmatite field, calibrating a portable laser-induced breakdown spectroscopy (pLIBS) equipment to identify and analyze these minerals may be challenging. Forty-nine samples of spodumene, petalite and spodumene + quartz were collected from 22 aplite-pegmatites from the BA field and sent for inductively coupled plasma-mass spectroscopy analysis. One calibration for both spodumene and petalite has been proven to be impossible since almost all the LIBS intensity ratios, including for Li, overlapped on both minerals. Thus, three calibrations were developed: one qualitative to distinguish both minerals and two more quantitative, specifically made for each mineral. The first LIBS calibration only has Fe since it is the sole element with intensity ratios different enough to distinguish both minerals. Eleven calibration lines were created: Li, Al, Si, Be, Na, P, K, Mn, Fe, Rb and Cs; however, only the Li, Al, and Si have consistent errors below 20%. Thin sections were produced and observed with optical microscopy and cathodoluminescence (CL) to control the purity and mineral paragenesis of the samples. The petalite pellets were also controlled with cold CL since petalite crystals often present fine spodumene and quartz inclusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".