Effect of water salinity and sodicity on soil least limiting water range
Bibliographic record
Abstract
Abstract This study aimed to evaluate the effects of water salinity and sodicity on the least limiting water range (LLWR) of two clay loam and sandy loam soils. The undisturbed soil samples were subjected to different water qualities, including three levels of sodium adsorption ratio (SAR, 1, 5, and 12) and electrical conductivity (EC, 1, 6, and 10 dS m −1 ). Our findings indicate that increasing EC at each SAR led to greater soil water retention. This was attributed to salinity affecting pore size distribution toward smaller pores by altering the diffuse double layer and causing soil particle flocculation. With increasing SAR levels at each EC level, soil water content at the wilting point also rose due to structural changes, clay swelling, and dispersion, resulting in more micropores and increased adsorptive surfaces in the soil. Additionally, soil volumetric water content at a 10% air‐filled porosity decreased, while values at a critical penetration resistance of 2 MPa increased with higher bulk density across all treatments. The LLWR showed a negative correlation with bulk density in clay loam soil across all SAR and EC treatments. The LLWR increased with higher water EC but decreased with increasing water SAR. The highest LLWR was observed at SAR = 1 and EC = 10 dS m −1 , while the lowest occurred at SAR = 12 and EC = 1 dS m −1 . The results revealed that elevated values of SAR in irrigation water reduced soil water accessibility for plants. However, as irrigation water salinity increased, the detrimental effects of SAR diminished.
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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 | 0.000 |
| 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 | 0.000 |
| 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".