Composts Containing Natural and Mg‐Modified Zeolite: The Effect on Nitrate Leaching, Drainage Water, and Yield
Bibliographic record
Abstract
The growing demand for environmental protection and sustainable food production requires the efficient use of organic and slow‐release fertilizers in agriculture. In this study, co‐composting of municipal solid waste (MSW) + three different ratios of natural and Mg‐modified zeolites (5, 10, and 15% on a wet weight basis) is conducted to improve the MSW compost quality. The effects of soil amendment with MSW compost containing natural zeolite (CNZ) and Mg‐modified zeolite (CMZ) on the corn yield, moisture content, leaching volumes, and NO3‐N concentrations are investigated. Compared to the control (zeolite‐free compost), the CNZ15 and CMZ15 treatments show 39.9 and 49.3% reduction in electrical conductivity and an increase of 64.5 and 110% in NH4‐N retention, respectively. By using the composts containing zeolite, the moisture content in the surface layer of soil is increased up to 12.6%. Nitrogen uptake and water use efficiency in the CNZ treatment are enhanced by 34.6 and 40.0%, respectively. The increase of the nitrogen uptake and water use efficiency of the CMZ treatment is 54.5 and 55.6%, respectively. Compared to the control, the amount of total NO3‐N leached from CNZ and CMZ treatments is decreased by 21.0 and 28.9%, respectively. The use of the MSW compost modified with Mg‐zeolite is, therefore, an environmentally friendly solution to prevent surface and groundwater pollution. The modified compost could be also used for the improvement of the physicochemical properties of sandy loam soils.
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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) | 0.000 | 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 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".