Glacial dispersal of gahnite from the Izok Lake Zn-Cu-Pb-Ag VMS deposit, northern Canada
Bibliographic record
Abstract
The Izok Lake Zn–Cu–Pb–Ag volcanogenic massive sulphide (VMS) deposit in the Arctic region of Canada is one of the largest undeveloped Zn–Cu VMS resources in North America. In 2009, the Geological Survey of Canada initiated a detailed glacial dispersal study of the deposit focused on documenting its associated indicator mineral and till geochemical signatures. Glacial dispersal from the deposit is fan-shaped and was formed by an older SW ice flow and younger NW ice flow phases. Till samples contain chalcopyrite, sphalerite, galena, and pyrite up to 1.3 km down-ice and gahnite at least 40 km down-ice. Gahnite (ZnAl 2 O 4 ) is an ideal indicator mineral in till because of its visually distinctive bluish green colour combined with its high specific gravity (4–4.6) for recovery using density-based separation methods, moderate hardness (physical durability during glacial transport), chemical stability in oxidizing surficial environments (resistance to post-glacial weathering), and its occurrence in highly metamorphosed VMS deposits such as Izok Lake. Most gahnite grains in till down-ice are 0.25–0.5 mm in size. Coarser gahnite (0.5–2.0 mm) occurs only in till proximal to the deposit (<3 km down-ice) and thus is an indicator of proximity to a gahnite-bearing bedrock source. Ore (Cu, Pb, Zn, Ag) and pathfinder element (As, Cd, Bi, Hg, In, Sb, Sb, Tl) contents in the <0.063 mm fraction of till reflect glacial dispersal up to a maximum of 6 km down-ice. A 15–20 km till indicator mineral sample spacing is sufficient to detect a gahnite glacial dispersal train such as that from the Izok Lake VMS deposit.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".