Automated Indicator Mineral Analysis of Fine-Grained Till Associated with the Sisson W-Mo Deposit, New Brunswick, Canada
Bibliographic record
Abstract
Exploration under thick glacial sediment cover is an important facet of modern mineral exploration in Canada and northern Europe. Till heavy mineral concentrate (HMC) indicator mineral methods are well established in exploration for diamonds, gold, and base metals in glaciated terrain. Traditional methods rely on visual examination of >250 µm HMC material. This study applies mineral liberation analysis (MLA) to investigate the finer (<250 µm) fraction of till HMC. Automated mineralogy (e.g., MLA) of finer material allows for the rapid collection of precise compositional and morphological data from a large number (10,000–100,000) of heavy mineral grains in a single sample. The Sisson W-Mo deposit has a previously documented dispersal train containing the ore minerals scheelite, wolframite, and molybdenite, along with sulfide and other accessory minerals, and was used as a test site for this study. Wolframite is identified in till samples up to 10 km down ice, whereas in previous work on the coarse fraction of till it was only identified directly overlying mineralization. Chalcopyrite and pyrite are found up to 10 km down ice, an increase over 2.5 and 5 km, respectively, achieved in previous work on the coarse fraction of the same HMC. Galena, sphalerite, arsenopyrite, and pyrrhotite are also found up to 10 km down ice after only being identified immediately overlying mineralization using the >250 µm fraction of HMC. Many of these sulfide grains are present only as inclusions in more chemically and robust minerals and would not be identified using optical methods. The extension of the wolframite dispersal train highlights the ability of MLA to identify minerals that lack distinguishing physical characteristics to aid visual identification.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".