Testing Pyrrhotite Trace Element Chemistry as a Vector Towards the Mineralization in the Sullivan Deposit, B.C.
Bibliographic record
Abstract
Mineral exploration methods are expensive and time-consuming, especially in recent times, where many near-surface deposits have been found and exploited. To overcome these challenges, new strategies must be developed. Here, we test whether the trace element chemistry of pyrrhotite changes systematically with distance from mineralization at the Sullivan deposit, British Columbia. If so, this could provide an additional tool to search for new ore bodies. Forty samples of the hanging wall, footwall, and mineralization hosting stratigraphy (host horizon) were collected from seven drill holes, both proximal and distal to the Sullivan deposit. These samples were analyzed using reflected light microscopy, an electron microprobe, and LA-ICPMS (laser ablation, inductively coupled plasma mass spectrometry). A total of three hundred and ninety LA-ICPMS analyses were used to build machine learning classifiers (cluster analysis and random forests) to determine whether an unknown pyrrhotite sample was from the mineralized horizon and, if so, whether it was proximal or distal to the mineralization. Our study found that the trace element abundance in pyrrhotite was higher in the footwall and hanging wall compared to the host horizon, and within the host horizon, was higher distal to the mineralization.
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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 | 0.000 | 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".