A review of radiometric analysis on soil erosion and deposition studies in Africa
Bibliographic record
Abstract
Soil erosion is one of the main soil degradation phenomena that threaten sustainable use of soil productivity thus affecting food security. In addition, it leads to reservoir storage capacity loss because of sedimentation. This not only affects water quantity but also water quality. Worldwide, annual loss in reservoir storage capacity due to sedimentation is 0.5 to 1%. Similarly, about 27% of land in Africa is largely degraded by erosion. As a result, there is need to minimize soil erosion and deposition through site specific estimation of soil erosion and deposition rates in the reservoirs. To achieve this, Fallout RadioNuclides (FRNs) are some of the methods in use. The most common radionuclides include; 137Cs, 210Pb and 7Be. Only few countries in Africa have exploited these FRNs. In these countries, 137Cs has been largely exploited but in some regions, it has been reported to be below minimum detection limit. Using 137Cs and 210Pb, maximum reference inventory in Africa is found to be 1450 and 2602 Bq/m2, respectively. However, there is minimal application of 7Be within the continent. Also, very little has been done in Africa to assess chronology and sedimentation rates of reservoirs using FRNs measured from sediment cores. In conclusion, a gap still exists on FRNs application in Africa in assessing soil erosion, deposition and reservoir sedimentation.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".