Principal component analysis of geochemical data from the REE-rich Maw Zone, Athabasca Basin, Canada
Bibliographic record
Abstract
Elemental assemblages derived from geochemical data are produced by geological processes, such as alteration and mineralization. However, processing large amounts of geochemical data that may reflect a variety of geochemical processes, can be a challenge. Methods such as principal component analysis (PCA) can be used to reduce the number of observed variables into a smaller number of artificial variables that account for most of the variance in a given dataset. This study uses RQ-mode PCA, which computes variable and object loadings simultaneously and displays the observations and the variables at the same scale. In order to assess elemental assemblages related to rare earth element (REE) enrichment, RQ-mode PCA was applied to total digestion data for 545 sandstone samples from the REE-rich Maw Zone in the Athabasca Basin, Saskatchewan, Canada. PCA biplots show HREE-Y-P enrichment, suggesting that xenotime is most likely the dominant host of HREEs, whereas LREE-Sr-Th-P enrichment may reflect monazite and/or aluminum phosphate-sulphate minerals as the host of LREEs. The positive correlation between U, Fe, V, and Cr suggests that oxidizing fluids likely introduced U. The 3D diagrams of principal components show that xenotime likely occurs in the upper members of sandstone Manitou Falls Formation (MFb, MFc, MFd) and monazite in lowermost Read Formation (RD Fm).
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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 | 0.003 | 0.006 |
| 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".