The hydraulic conductivity of sands with dispersed oversized particles
Bibliographic record
Abstract
Engineered fills, glacial tills, mudflows, debris flows, residual soils, and colluvial deposits have a structure consisting of a soil matrix (e.g. sand) and large dispersed particles (e.g. gravel) mixed in the matrix. Mixtures of this type have received little attention in soil mechanics. The purpose of this study was to evaluate the effect of dispersed particles on the permeability of sand-gravel mixtures. Constant head permeability tests (ASTM D 2434-68) were conducted on samples having 63.5 mm in diameter and 155 mm in height. The samples were made of a matrix of Ottawa sand (dave= 0.725 mm) and dispersed particles with an average diameter of 11.1 mm. The percentage by volume of gravel in the mixtures was varied between 0% and 14%. The results indicated that the permeability of the mixture decreased as the volume percentage, B, of gravel in the matrix increased. It was determined that the permeability of the mixture, Km, can be obtained from the permeability of the sand matrix, Ks, and the percentage by volume, B, of the gravel in the mixture by using the relationship: Km=Ks[(1−B)/(1+B/2)]. Only Ksof the sand matrix and the percentage by volume, B, of the dispersed gravel need to be known in order to obtain the permeability of the mixture, Km. The effect of clusters of dispersed particles in the sample was also investigated. It was found that the permeability of mixtures with clusters depends on the relative location of the clusters within the mixture.
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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 | 0.000 | 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".