Soil conditions in the “donga” soils in subhumid zone in West Africa
Bibliographic record
Abstract
Water erosion threatens large areas around the world. “Donga” is one of the witnesses of gully erosion in northern Benin, which induces serious threats to the natural habitats. This study was conducted to evaluate soil moisture content in different donga types (“microdongas”, “mesodongas”, and “megadongas”) and its variation at different topographic levels. The thermogravimetric soil moisture measurement technique was used for moisture estimation on saturated and unsaturated soil. Data were analyzed through analysis of variance test and t test with SAS software. The results showed that soil moisture content varied according to donga types. On unsaturated soil, higher difference (2.75%, p = 0.0328) was obtained in mesodongas at the middle followed by megadongas at the middle (2.6%, p = 0.034). On saturated soil, higher difference was obtained in mesodongas at the upstream (6.51%, p < 0.0001) at downslope (4.55%, p = 0.0032) and in the middle (4.32%, p = 0.0328) followed by microdongas at the upstream (2.25%, p < 0.0001). The findings in this paper should be useful to researchers looking for soil moisture information in subarid and subhumid zone at different topographic levels to develop afforestation strategies based on species that can make the best use of soil water.
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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 | 0.001 | 0.001 |
| 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".