Identifying common protoliths for altered rocks using clustering and classification of geochemical data at the Minto Copper-Gold Mine, Yukon, Canada
Bibliographic record
Abstract
Rocks in metalliferous ore deposits are typically classified as either ore or waste rock. An alternative distinction, more helpful for discriminating alteration haloes around ore, is protolith rock and altered rock. Metasomatic alteration occurs during hydrothermal ore deposition when mineralising fluids are in disequilibrium with surrounding host rocks. Geologists in brownfields areas commonly seek to interpret ore body geometry and surrounding alteration halos using the spatial distribution of whole rock chemical analyses. However, modelling the relative highs and lows in geochemical data does not give a complete representation of alteration geometry; weight percent values provide no direct information about the nature and magnitude of mass transfer during hydrothermal alteration. The compositional data of rocks are inherently multivariate and the concentrations of all constituents are related to each other by the process of closure. For example, in a simplified three element system, reducing one element will lead to an apparent enrichment of the other two.Here we present a workflow to process a whole rock geochemical dataset and produce labels for groups of similar least-altered protolith rocks, and then apply these labels to groups of altered rocks. This method involves quick means of exploratory data analysis and lends itself to iterative refinement, relying on a geologist to use their knowledge of a particular site.
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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 | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".