Mineralogical Variability of the Whabouchi Pegmatite and its Effect on the Li Concentrations
Bibliographic record
Abstract
ABSTRACT The Whabouchi pegmatite, located in the James Bay area of Québec, is a lithium-cesium-tantalum pegmatite of albite-spodumene type. In order to evaluate the mineralogical and geochemical variability of the pegmatite, 168 samples were collected from drill core and analyzed for their whole rock geochemistry and mineralogy. The pegmatite is composed of quartz, albite, K-feldspar, spodumene, and muscovite, with trace amounts of spessartine garnet, apatite, beryl, tourmaline, and oxides. It is mostly homogenous, showing greatest variability with respect to the minerals albite, K-feldspar, and spodumene. The Li2O concentration varies between 0.03 and 4.46 wt.%, for an average of 1.53 wt.% and an estimated variability of 53%. Modal mineralogical data showed an inverse correlation between spodumene and the feldspars (albite + K-feldspar), which could also be observed when comparing the Li2O content with the sum of Na2O and K2O. To improve on this relationship, correlation matrices comparing all geochemical components were constructed and allowed the development of an equation able to estimate the Li2O content of the samples within 0.5 wt.% of the measured value. The applicability of the equation to other albite-spodumene type pegmatites was verified with samples collected from the Georgia Lake area of northwestern Ontario. The calculation provided a good approximation of the Li2O content of the samples, with 92% of the data showing a difference of 0.5 wt.% Li2O or less; however, it has an impact on the statistical mean of the data set, the data being recalculated to return an average closer to 1.5 wt.% Li2O. Considering the difficulty in analyzing lithium content of an in situ sample, this relationship could facilitate estimation in the field using portable X-ray fluorescence with the capability of analyzing all required components.
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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 | 0.001 | 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".