Indicator minerals in fine-fraction till heavy-mineral concentrates determined by automated mineral analysis: examples from two Canadian polymetallic base-metal deposits
Bibliographic record
Abstract
Exploration under glacial sediment cover is a necessary part of modern mineral exploration in Canada. Traditional indicator methods use visual examination to identify mineral grains in the 250 to 2000 µm fraction of till heavy-mineral concentrates (HMC). This study tests automated mineralogical methods using scanning electron microscopy to identify indicator minerals in the fine (<250 µm) HMC fraction of till. Automated mineralogy of polished grains from the fine HMC enables rapid data collection (10 000-300 000 grains/sample). Samples collected near two deposits were used to test this method: four from the upper-amphibolite facies Izok Lake volcanogenic massive-sulfide deposit, Nunavut, and five from the Sisson granite-hosted W-Mo deposit, New Brunswick. The less than 250 µm HMC fraction of till samples collected down ice of each deposit contain ore and alteration minerals typical of their deposit type. Sulfide minerals occur mainly as inclusions in oxidation-resistant minerals, including minerals previously identified in each deposit's metamorphic alteration halo, and are found to occur farther down ice than the grains identified visually in the greater than 250 µm HMC fraction. This project's workflow expands the detectable footprint for certain indicator minerals and enhances the information that can be collected from till samples.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".