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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".