Effects of ionized brackish water and polyacrylamide application on infiltration characteristics and improving water retention and reducing soil salinity
Bibliographic record
Abstract
There is an urgent need for brackish groundwater-based irrigation methods to be developed for saline soils that are effective, economically advantageous, and environmentally friendly. The use of both ionized brackish water and polyacrylamide (PAM) might provide such a method. The long-term use of brackish water irrigation can lead to the secondary salinization of soil and, as a consequence, restrict the development of the agricultural economy. Here, we conducted one-dimensional vertical infiltration experiments to examine the effects of ionized brackish water and PAM on soil infiltration characteristics. The result indicated that the water retention of soil first increased and then decreased with the increased in PAM application rates. The maximum water retention of soil was obtained in PAM application of 0.04% for ionized brackish water treatment. Soil water storage for the 0.04% PAM application under ionized brackish water irrigation was the highest and 5.1% higher compared with non-ionized brackish water at a PAM application rate of 0.04%. The ionized brackish water treatment at a PAM application rate of 0.04% improved the desalinization efficiency by 2.3% compared with non-ionized brackish water treatment. Thus, ionized treatment and PAM application are effective for improving the characteristics of soil water and salt transport and permit the safe use of brackish groundwater for irrigation.
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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 | 0.000 | 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".