Impact of soil texture on biosurfactant‐mediated soil wetting and water retention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Increasing global food demand combined with more frequent and intense periods of drought necessitates new strategies to improve agricultural water use efficiency. Amending soils with biosurfactants provides a method to increase soil wettability and improve soil water retention, thereby reducing freshwater demand. This study evaluates the impacts of soil texture on soil water retention after amendment with the biosurfactant, surfactin. Texture effects were systematically investigated by mixing silty clay loam soil with Ottawa sand, ensuring chemically equivalent soil constituents. Sandy loam texture exhibited the most significant response after 50 mg kg −1 surfactin treatment, indicated by a 25% water contact angle decrease and a twofold increase in soil water retention after a 48‐h dryout period. In contrast, all other soil textures, including silty clay loam, loam, and loamy sand, had no significant improvements. These findings highlight the critical role of soil texture on biosurfactant efficacy for optimized application in agricultural soils. Core Ideas Soil texture plays a critical role in biosurfactant amendment efficiency for improving soil water retention. Texture effects were isolated using mineralogically equivalent soils of varied textures. Sandy loam was the only texture with improvements in wettability and water retention after surfactin amendment. Biosurfactant amendments can increase the economic value of sandy loam soils by improving water retention.
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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 | 0.000 | 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 it