Application of sulphur isotopes to discriminate Cu–Zn VHMS mineralization from barren Fe sulphide mineralization in the greenschist to granulite facies Flin Flon–Snow Lake–Hargrave River region, Manitoba, Canada
Bibliographic record
Abstract
The Flin Flon Belt in northern Canada is one of the largest Palaeoproterozoic volcanic-hosted massive sulphide (VHMS) districts in the world, but up to 20 000 km 2 of prospective Palaeoproterozoic basement south of this belt is buried beneath 10 to 100 m of Phanerozoic calcareous cover. The recent acquisition of airborne SPECTREM geophysics data south of the Flin Flon Belt has resulted in the discovery of Cu-Zn sulphide prospects comprising pyrrhotite, pyrite, chalcopyrite, sphalerite and galena, but numerous barren Fe sulphide occurrences comprising only pyrite and pyrrhotite have also been intersected. The problem for explorers is trying to determine whether a barren Fe sulphide intersection that has just been cored is part of a larger Cu-Zn mineralized system, or nothing more than a pyrite–pyrrhotite occurrence. A sulphur isotope study of sulphides from the Flin Flon–Snow Lake–Hargrave River–Talbot area shows that sulphides from the Cu-Zn VHMS deposits have δ 34 S values that range between −1.4 and 6.4‰, with a mean δ 34 S value of 1.6 ± 1.7‰ (2σ error). More than 95% of these samples have δ 34 S values of <3.3‰. In contrast, pyrite and pyrrhotite separates from barren Fe sulphide deposits have δ 34 S values between 1.8 and 10.0‰, with a mean δ 34 S value of 4.3 ± 1.8‰ (2σ error). In this case, >84% of these samples have δ 34 S values of >3.3‰. The results imply that the barren Fe sulphide deposits can be statistically distinguished from Cu-Zn VHMS mineralization based on S isotopic composition, which should make future exploration drilling decisions easier.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".