CLASSIFICATION OF CATCHMENT RISK AREAS USING SPATIALLY DISTRIBUTED EVENT-BASED SOIL EROSION: A CASE OF UPPER NJORO RIVER CATCHMENT, KENYA
Bibliographic record
Abstract
Human-induced soil erosion and drastic change in land use practices have adversely infl uenced the land degradation and surface runoff response in upper Njoro River catchment. The drainage area is approximately 127 km 2 . Due to human activities, the land has been exposed to accelerated erosion and low land productivity, water scarcity, decline in ground water recharge, siltation of Lake Nakuru and other sediment sinks. This study was conducted to establish event-based risk areas for prioritized conservation within the catchment. Spatially distributed soil erosion map was created as a ratio of sediment yield to sediment delivery ratio (SDR). Modifi ed Universal Soil Loss Equation (MUSLE) integrated within a Geographical Information Systems environment was used to create the sediment yield map. Spatial data layers for the MUSLE were derived from a 20-m resolution Digital Elevation Model, soil property, land use maps and climatic data of the catchment. Land use map was derived from Landsat imagery via its processing using Integrated Land and Water Information Systems software. The results show that the spatially distributed soil erosion ranged from 0.06 to 0.51 t/ha for a 43.2-mm rainfall event. Spatially distributed SDR ranged from 0.09 to 0.82, while the average SDR for the whole catchment was 0.72. These values were derived using an empirical equation. A new contribution was made by developing spatially distributed slope length factor, SDR, runoff volumes, MUSLE parameters and classifi ed erosion risk areas for prioritized catchment conservation. This means that the event-based soil erosion classifi cation can be adopted for prioritized soil and water conservation within Njoro catchment.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".