Density Segregation of Minerals During High-Velocity Transport Over a Rough Bed: Implications for the Formation of Placers
Bibliographic record
Abstract
Preferential accumulation of minerals with high densities forming placer deposits in longitudinal bars of braided rivers relies on the segregation of this denser material from the quartz and feldspar sands dominating sediment in transport. Flume experimentation conducted on mixtures of quartz, pyroxene, magnetite, and lead sand transported over rough cobble beds demonstrated that the sand distribution was more uniform above the bed than theory predicted. The deviation from theory was inversely proportional to the density of the mineral. Fall velocity was not a good predictor of the amount of a mineral species carried in suspension, as coarse-grained quartz dominated the suspension population in the flume runs where lead was a major constituent in the traction population, even though the quartz had a greater fall velocity. Applying these results to placer deposits indicates that during flood events, even the smaller dense minerals and especially the super-dense minerals will be transported with the gravel and cobble load, whereas the quartz and feldspar will be spatially segregated during transport and temporally and spatially segregated during deposition. This mechanism of placer formation is capable of creating the types of deposits present in orebodies and showings such as the uraniferous paleoplacers at Elliot Lake, Canada, and the gold-bearing longitudinal bars in the Witwatersrand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".