Interactive Effects of Microbial Fertilizer and Soil Salinity on the Hydraulic Properties of Salt-Affected Soil
Bibliographic record
Abstract
Significant research has been conducted on the effects of fertilizers or agents on the sustainable development of agriculture in salinization areas. By contrast, limited consideration has been given to the interactive effects of microbial fertilizer (MF) and salinity on hydraulic properties in secondary salinization soil (SS) and coastal saline soil (CS). An incubation experiment was conducted to investigate the effects of saline soil types, salinity levels (non-saline, low-salinity, and high-salinity soils), and MF amounts (32.89 g kg−1 and 0 g kg−1) on soil hydraulic properties. Applied MF improved soil water holding capacity in each saline soil compared with that in CK, and SS was higher than CS. Applied MF increased saturated moisture, field capacity, capillary fracture moisture, the wilting coefficient, and the hygroscopic coefficient by 0.02–18.91% in SS, while it was increased by 11.62–181.88% in CS. It increased soil water supply capacity in SS (except for high-salinity soil) and CS by 0.02–14.53% and 0.04–2.34%, respectively, compared with that in CK. Soil available, readily available, and unavailable water were positively correlated with MF, while soil gravity and readily available and unavailable water were positively correlated with salinity in SS. Therefore, a potential fertilization program with MF should be developed to increase hydraulic properties or mitigate the adverse effects of salinity on plants in similar SS or CS areas.
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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".