Assessment of Improved Ladder Terraces in Controlling Soil Erosion on Uluguru Mountains-Tanzania
Bibliographic record
Abstract
This study assesses effectiveness of improved ladder terraces in controlling soil erosion on steep slopes of Uluguru Mountains in Morogoro Region, where runoff collection tanks were located downstream of the divisor system were all runoff from the catchment upstream where improved ladder terraces were located. The soil properties percentage weight for sand, silt and clay were average 40, 10 and 50 respectively. Half of the terraces were left barely and half were planted with maize crop. Runoff and soil loss generated during every rainstorm was collected from six field plots of improved ladder terraces to the tanks. There was statistically significant different between reductions of soil loss in bare improved ladder terrace and cropped improved ladder terrace. The amount of runoff on the bare soil was high by 15% to 18% compare to runoff on cropped soils. It was found that cropped improved ladder terrace reduced soil loss by 74% while bare improved ladder terrace reduced soil loss only by 41%. Simple linear regression shows runoff water generated from rainfall amount with soil losses from different land cover. Bare soils behave with linear relationship (r² = 0.85) unlike cropped soil were r² = 0.36 because of gradual increase of crop canopy at every crop stage hence less impact to the soil. Soils losses from the bare soil ladder terrace and that of cropped soil ladder terrace was significantly difference with correlation coefficient of 0.863 at vegetative stage and 0.928 at the full booting stage.
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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".