Review of gold and platinum group element (PGE) indicator minerals methods for surficial sediment sampling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it