Automated indicator-mineral analysis of the fine-sand heavy-mineral concentrate fraction of till: a promising exploration tool for porphyry copper mineralization
Bibliographic record
Abstract
Previous studies indicated that heavy mineralogy (specific gravity (SG) = 2.8-3.2 and >3.2) of the medium-sand fraction (0.25-0.50 mm) of till contains porphyry copper indicator minerals (PCIM) derived from mineralization or alteration zones. To improve the PCIM method for mineral exploration, we analyzed the heavy mineralogy (>3.2 SG) of the fine-sand fraction (0.125-0.180 mm) of till using an automated method that combines scanning electron microscopy (SEM) and mineral-liberation analysis (MLA). The MLA-SEM method identifies mineralogy based on grain composition determined by SEM-energy dispersive spectroscopy. The distributions of epidote and chalcopyrite in till at four porphyry copper deposits in British Columbia show similarities between the fine-sand fraction analyzed by MLA-SEM and the medium-sand fraction analyzed by optical mineralogy: both show dispersal parallel to ice-flow movements. Analyzing the fine-sand, heavy-mineral concentrate (HMC) fraction of till by MLA-SEM can be used in exploration for porphyry copper mineralization. We estimate 5 to 8 kg of bulk till is sufficient to prepare 0.3 g aliquots of fine-sand HMC for MLA-SEM; this is smaller than the 9 to 15 kg required for optical mineral analysis of the medium-sand HMC fraction. Smaller field samples can lower costs for reconnaissance mineral exploration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".