Improving lithological discrimination in exploration drill-cores using portable X-ray fluorescence measurements: (2) applications to the Zn-Cu Matagami mining camp, Canada
Bibliographic record
Abstract
A new geoscientific application of portable XRF (pXRF) analysers is the acquisition of high-spatial resolution down-hole geochemical profiles obtained in-situ on exploration drill-cores. One advantage of such profiles over traditional laboratory geochemistry, apart from the non-destructive aspect of pXRF, is that they are obtained quickly, in the field. So they can help exploration companies take important decisions such as “has a target stratigraphic horizon been reached, or should we drill deeper?” For example, in the Matagami mining camp, pXRF data permits the rapid distinction of two visually similar and variably altered rhyolites in the Persévérance area, based on a plot of Ti/Zr vs Al/Zr. The corrected pXRF data plot within the same fields as the traditional geochemical analyses for these rhyolites. Another advantage of pXRF profiles for exploration companies, geological surveys or academic researchers is the ability to locate lithological contacts better, and in general improve down-hole lithological discrimination, especially for fine-grained and/or hydrothermally altered lithologies. For example, in the Caber volcanogenic massive sulphide deposit area, there are abundant intrusions which makes it difficult to follow the volcanic stratigraphy between drill-holes and sections. In the drill-hole studied, the pXRF data, plotted as down-hole profiles of elements/oxides and ratios, allow several previously unidentified altered dykes to be distinguished from altered rhyolites.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".