Physicochemical Analysis of Parameters Influencing Soil Loss for a Selected Location in North Central Nigeria Using Rainfall Simulator
Bibliographic record
Abstract
Developing a simple and proper model that can accurately predict runoff generation for various locations is in strong demand. This study developed a simple model based on the interactive effects of rainfall intensity and soil physicochemical properties on runoff using a locally produced rainfall simulator. The drop velocity (DV) was calculated to be 8.101m/s and 2.443 m/s when operated at maximum and minimum intensity, respectively, and the performance test revealed the experimental coefficient of uniformity (CU) and rainfall intensity from the simulator to be 79.86 % at 31.79 mmhr-1 and 78.03 % at 16.08 mmhr-1 at maximum and minimum intensity respectively. Results showed that the soils were loamy sand, with clay having the lowest percentage between 3.55% - 4% and sand having the highest percentage between 78.4% - 80.1% on both plots. Runoff significantly correlated with pH(H20), nitrogen and rainfall intensity for vegetative plot (p < 0.001, R2 = 86.29%) while for bare plot, runoff significantly correlated with pH (KCl), Electrical Conductivity, Exchangeable Calcium, and rainfall intensity (p < 0.001, R2 = 92.39%). This result revealed that rainfall intensity and alkalinity are key factors influencing runoff in the study location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".