Reducing Evaporation from Arid Soil in Jordan Through Absorbent Materials: Volcanic Tuff, Wood Ash, and Date Pit Ash
Bibliographic record
Abstract
Irrigation water represents the primary water usage in arid regions worldwide, reaching up to 70% in some countries.However, more than 90% of that water is lost by evaporation in arid regions.This study aims to reduce the soil evaporation rate in Jordan, a water-scarce country, using water-absorbent materials that are both affordable and environmentally friendly.Our research used three absorbent materials: volcanic tuff, wood ash, and date pit ash.These materials were characterized by different analytical methods to investigate their chemical composition, mineral content, specific surface area, and microstructural morphologies.Three different soil, absorbent materials, and water mixtures were prepared in specific ratios.The evaporation rates for the mixtures were estimated in an open area using a time-domain reflectometry sensor during the winter of 2022.The soil temperature, relative humidity, and wind speed conditions were recorded.It was found that these materials have a significant efficiency in reducing the evaporation rate related to their internal structure.The study suggests utilizing biomass ash to alter the internal structure of soil aggregates, thereby improving water retention and lowering evaporation rates.This action would diminish the need for irrigation, consequently bolstering sustainable water resources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".