K-Feldspar Geochemistry as an Indicator of Lithium Mineralization in the Barroso-Alvão Aplite-Pegmatite Field, Northern Portugal
Bibliographic record
Abstract
Abstract Inductively coupled plasma-mass spectrometry analysis was conducted to examine the geochemical composition of K-feldspars from various aplite-pegmatites in the Barroso-Alvão field, focusing on the differences between Li-rich and Li-barren aplite-pegmatites. The study revealed significant variations in the concentrations of minor and trace elements (Rb, Tl, Li, Ga, Pb, Cs, Ba, Be, Ta, and Sn) present in the K-feldspars of Li-barren, spodumene-rich, and petalite-rich aplite-pegmatites. The data also indicate a geographical trend in both mineralogy and geochemistry across the aplite-pegmatites of the Barroso-Alvão field. Li-barren aplite-pegmatites are more concentrated in the southeast, spodumene-rich dominate the center, and petalite-rich varieties are more common in the northwest. Additionally, portable X-ray fluorescence analysis was performed on the crystals of the same samples to evaluate the feasibility of in situ geochemical analysis of K-feldspars, aiming to determine whether an aplite-pegmatite can be quickly identified as Li-rich. This approach seeks to provide a rapid field assessment of whether an aplite-pegmatite justifies further exploration for Li mining. Notably, the trace amounts of Li, Sn, P, and Ta found in K-feldspars are likely due to mineral inclusions of spodumene, cassiterite, apatite, and columbite–tantalite minerals, as observed petrographically in one of these Li-rich aplite-pegmatites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".