Review of gold and platinum group element (PGE) indicator minerals methods for surficial sediment sampling
Bibliographic record
Abstract
ABSTRACT For gold deposits, varying combinations of gold grains, sulphides, platinum-group minerals (PGM), tellurides, scheelite and rutile, and some secondary minerals are useful indicator minerals depending on the deposit type, bedrock geology and weathering regime. Gold grain size, shape, and chemical composition for a variety of sediment types, including stream and glacial sediments, have been documented and the data used to determine potential source rocks and distance of transport. Useful indicator minerals for PGE deposits include those oxide and silicate minerals that indicate the host rocks and PGM, gold, sulphides, arsenides and antimonide minerals that indicate mineralization. Composition and morphology of PGM also have been well documented and this information is used to determine their genesis, potential source rocks and transport distance. Gold grains have been recovered from glacial and stream sediments for more than 100 years. PGM grains have a similar long history of recovery from streams, but only a few cases of recovery from glacial sediment have been reported. Research has focused on the development of microchemical characterization techniques for placer gold and PGM, while the focus for indicator minerals from glacial sediments has been the characterization of oxide and silicate suites.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".