Physical features indicating the glacial transport distance of gahnite from the Izok Lake Cu-Zn-Pb-Ag VMS deposit, Nunavut
Bibliographic record
Abstract
This small study of gahnite from the Izok Lake deposit and associated glacial dispersal train northwest of the deposit is the first to evaluate and report on the shape and surface characteristics of gahnite in glacial sediments. It demonstrates that increased distance of glacial transport results in: 1) a significant decrease in the proportion of attached quartz and muscovite on individual gahnite grains; 2) no discernable wear on the actual gahnite grains; 3) a significant decrease in the number of gahnite grains in till; and, 4) reduced grain sizes. Further work is recommended on other gahnite glacial dispersal trains to confirm and refine the proposed classification system and, in particular, to determine the distances at which a) none, and b) all, of the gahnite grain are completely free of quartz and muscovite. Future studies should ideally include only till samples where gahnite populations have been picked to completion, and no grains have yet been removed for other uses such as EMP analysis, in order to avoid any bias. In samples with very large gahnite populations, a split should be picked to completion rather than trying to pick a representative population of grains from the entire sample. Whereas quartz and muscovite are universal gahnite associates in all of the significant gahnite glacial dispersal trains that Overburden Drilling Management Limited has examined to date, other minerals such as spessartine may occur in sufficient concentrations in some metamorphosed VMS alteration zones to be in direct contact with gahnite and thus potentially to adhere to some of the dispersed grains.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".