Laboratory evaluation of wicking geotextile for moisture reduction in silty sands at different fines contents
Bibliographic record
Abstract
The effect of fines content on the performance of the wicking geotextile is not clear. This study developed a simple moisture reduction test method to quantify the effectiveness of the wicking geotextile in reducing moisture in silty sands at four different fines contents and four different waiting periods. The sand was prepared at an initial moist condition based on its average field moisture capacity. A wicking or non-wicking woven geotextile was placed in the middle of a soil column. The effect of a geotextile on the moisture content of the sand was evaluated by measuring their gravimetric moisture contents at different distances from the geotextile at different times. Test results show that the amount of moisture reduced by the wicking geotextile decreased with the content of fines in the silty sand. On the contrary, the non-wicking geotextile obstructed water flow, hence moisture accumulated on it. The moisture content profile in the soil column indicated the influence zone by the wicking geotextile in the silty sand, which depended on the fines content. The percent of soil moisture content reduction by the wicking geotextile identified the limit of the fines content for the effectiveness of the wicking geotextile. • This paper investigated the effect of fines in silty sands on the effectiveness of wicking geotextile in moisture reduction. • A simple soil column test was developed for this investigation. • Wick geotextile effectively reduced moisture content of silty sand with up to 15% fines while non-wicking woven geotextile served as a barrier to downward movement of moisture.
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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 | 0.000 | 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".