Permeability Test Results With Packed Spheres and Non-Plastic Soils
Bibliographic record
Abstract
Abstract This paper examines permeability test results with packed spheres and non-plastic soils. Most tests on packed spheres were performed in physics and chemistry, using liquids or gases to measure the intrinsic permeability, K (m2). All K (m2) were converted into saturated hydraulic conductivity ksat (m/s). Equal spheres and spheres having a unimodal or multimodal grain size distribution curve (GSDC) were tested. We point out interesting differences between the approaches in physics, geotechnique, and hydrogeology, then discuss difficulties with testing methods, before analyzing all test data. We propose a method to fit a GSDC with either a single, or a sum of lognormal equations, which gives a closed-form expression for the specific surface. Then, we assess the performance of predictive methods for ksat, including the Kozeny–Carman equation. A few methods, which use only a mean particle size, are shown to give excellent predictions for equal spheres and unimodal packings. Other methods, which use the effective size d10 and the void ratio e, are also shown to give excellent predictions for packed spheres and non-plastic soils, in the ranges for which they were initially developed. A new equation is proposed, which successfully predicts ksat in the range from 1 to 10−10 m/s, when the parameter [d102 e3/(1+e)] varies from 10−9 to 102 mm2, for spheres and non-plastic natural soils.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".