Evaluating white mica as an indicator mineral for lithium bearing pegmatites, Wekusko Lake pegmatite field, Manitoba, Canada
Bibliographic record
Abstract
This project investigates the potential use of white micas as an indicator mineral within Li-bearing pegmatites and the potential of field portable techniques, such as Raman spectroscopy and Laser Induced Breakdown Spectroscopy (LIBS) as real-time techniques in exploration. The pegmatites in the Wekusko Lake field, Manitoba, Canada, display five zones of varying mineralization. White micas display two textures in the field (primary igneous and secondary) and four textures were identified by backscattered electron imaging (poor zonation, rimmed, patchy and exsolution). The white micas were analysed by Electron Probe Micro-Analysis (EPMA) and Laser Ablation Induction-Coupled Plasma Mass Spectroscopy (LA-ICP-MS) and the results show a strong correlation in the Li content of the white mica and the whole rock Li2O obtained from the assays of drill core. The K/Rb vs. Cs contents of the white mica indicate that the most prospective dikes contain moderate to highly evolved grains. The use of portable Raman Spectrometer, while useful for mineral identification, was not able to detect a significant Li signature at the concentrations tested (1500-6000 ppm).
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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 | 0.002 | 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".