Geotextile Filter Design Using Pore Size Distribution
Bibliographic record
Abstract
Current geotextile filter design methodologies rely on geotextile apparent opening size (AOS) measurements obtained from ASTM D 4751. Even though a typical geotextile sample has a range of opening sizes, the current standard of practice is to use ASTM D4751 to obtain a single AOS value that is used for filter design of soils with a wide range of particle sizes. The AOS is a property of the geotextile that indicates the approximate largest particle that would effectively pass through the geotextile. It is also commonly referred to as the O95 of the geotextile. In recent years, geotextile pore size distribution (PSD) is being obtained in accordance with ASTM D6767. PSD results yield a spectrum of opening sizes that represent a geotextile’s ability to retain soil particles, as opposed to a single “largest opening size” value that is obtained from AOS testing per ASTM D4751. A new geotextile filter design method using multiple retention criteria has been created through a synthesis of the existing body of knowledge combined with laboratory testing to validate the new design methodology.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".