Particle transport characteristics and filtration of granitic residual soils from the Korean peninsula
Bibliographic record
Abstract
Weathered granitic residual soils, which are found in much of the Korean peninsula, pose unique challenges in terms of internal stability and filtration. The particle transport and filtration behavior of two extreme soil types, named Shinnae-dong and Poi-dong, are investigated in this paper. The erodibilities of the two soils are evaluated using constant flow-rate experiments on both undisturbed samples and samples with cylindrical holes. A comparison of the results from these experiments revealed the extent of particle redeposition and self-filtration in the internal erosion process. In spite of the differences in the mineralogical properties and engineering characteristics of the two soils, the size of the eroded particles from the two soils fell within the same range of 1100 µm. The two soils were coupled with filters, chosen according to the US Bureau of Reclamation's (USBR) filter criteria, to determine the efficiency of filters in minimizing erosion. It was found that the filters significantly minimized the erosion of the two base soils. However, the associated reductions in filter permeability are greater than one order of magnitude. Experiments using filters alone with particulate suspensions as the influents enabled the evaluation of a coefficient, λ, which could be used to characterize the particle retention capacities of the filters.Key words: particle transport, residual soils, filtration, drainage, Korean peninsula, soil filter criteria.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".