Effect of Different Land Use Types on Nutrient Distribution across Soil Depth in Busega Wetland, Uganda
Bibliographic record
Abstract
Wetlands play a number of vital roles in the ecosystem, such as serving as nutrient sinks, preventing floods, storing carbon, and filtering water. Encroachment on wetlands has led to substantial economic and environmental losses, including water quality degradation, loss of biodiversity and natural habitats, reduced climate mitigation as well as social and health risks. This study evaluated the effect of different land use types on nutrient stock distribution across varying soil depths in Busega wetland. The soil samples were collected in three different land uses (annually cultivated areas, perennially cultivated areas, and the undisturbed wetland area) at three different depths (0 - 10 cm, 10 - 20 cm, and 20 - 30 cm) in 2021. The soil samples were analyzed for physicochemical soil properties including soil texture and nitrogen, phosphorus, calcium, and potassium concentrations. The interaction between land use type and soil depth did not have a significant effect on nutrient distribution. However, our results showed that the main effects of land use type and soil depth influenced nutrient stock distribution across the wetland. Higher nutrient concentrations were observed under perennial cropping system than in both annual cropping system and the undisturbed wetland area. Soils under perennial cropping systems had the highest soil organic matter (1.45%), calcium (2.06 Cmol/Kg) and potassium (0.091 Cmol/Kg) levels. Higher soil organic matter (1.40%), nitrogen (0.22%), calcium (1.74 Cmol/Kg), and potassium (0.07 Cmol/Kg) were found at the mid-soil depth of 10 - 20 cm. Our results show substantial nutrient changes due to agricultural activities in the Busega wetland, suggesting further research is urgently needed to determine if these changes have adverse effects on biodiversity and water quality of the wetland and nearby water resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".