Indicator mineral signatures of the Izok Lake Zn-Cu-Pb-Ag volcanogenic massive sulphide deposit, Nunavut: Part 2 till
Bibliographic record
Abstract
Volcanogenic massive sulphide (VMS) deposits have characteristic suites of indicator minerals; however, little research has been undertaken to determine which minerals may be useful to exploration in glaciated terrain. To date, few case studies have been conducted down-ice of known deposits. In response to this knowledge gap and to stimulate exploration in northern Canada, the Geological Survey of Canada, through its Geo-mapping for Energy and Minerals GEM Program (2008 - 2013), in collaboration with Queen's University, Minerals and Metals Group Limited (MMG) and Overburden Drilling Management Limited (ODM) examined the indicator minerals that characterize the Izok Lake Zn-Cu-Pb-Ag VMS deposit, Nunavut, and documented their glacial dispersal down-ice. Till samples down-ice of the Izok Lake deposit contain 10s to 1000s of grains/10 kg of chalcopyrite, sphalerite, galena, and pyrite up to 20 km down-ice (northwest). Gahnite is the most useful indicator of glacial dispersal from the Izok Lake VMS deposit because it is abundant in the deposit and till down-ice, sufficiently dense (specific gravity 4 - 4.6), physically robust, and easy to visually identify due to its distinctive blue-green colour. Gahnite in the area of the Izok Lake deposit is present in till at an abundance of 10 to 1000s grains/10 kg and was found at least 50 km down-ice. Staurolite may also be an indicator mineral of the deposit, but insufficient information is available about the modal abundance and ZnO content of staurolite in the deposit host rock. Sulphide minerals chalcopyrite, sphalerite, galena, loellingite, and pyrite are also useful indicators of the Izok deposit. However, they occur in low abundances in till down-ice from the deposit, and sulphide minerals are generally not physically and chemically robust, and therefore are unlikely to survive postglacial weathering of till.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".