Characterizing hydrological-sensitive areas of the Kinyerezi river sub-catchments in Dar es Salaam, Tanzania using the topographic index approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Several areas experience frequent floods due to anthropogenic activities. Among them, is the Dar es Salaam city, which experiences frequent floods along the Msimbazi River, whose flows originate from different tributaries including the Kinyerezi River. This study aims to evaluate the hydrological-sensitive areas of the Kinyerezi River sub-catchments using topographic index values (λ*) that enable the identification of areas with a higher probability of generating surface runoff. A digital elevation model was utilized to delineate the Kinyerezi River sub-catchment characteristics using ArcGIS 10.4. Soil infiltration rates (Ks) on selected open places were determined using a Guelph permeameter. Soil particle size distributions were analyzed and the λ* values were evaluated. The results showed the particle size distribution contains sand and silt-clay ranging from 46 to 84% and 16 to 53%, respectively. The Ks ranged from 0.6 to 7.8 mm/h while the sub-catchment KS3 scored the highest λ* value of about 10.7. Hence, there is a higher probability for generating surface runoff. Sub-catchment KS16 scored the smallest λ* value of 5.7, perceived to generate less surface runoff. Low-impact development practices capable of capturing runoff and enabling infiltration, evaporation, and detention should be employed in sub-catchments with higher λ* values.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it