Assessing Land Use and Land Cover Changes in the Murchison Bay Catchment of Lake Victoria Basin in Uganda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Murchison Bay catchment in the northern shoreline of Lake Victoria basin is a high valued ecosystem because of the numerous human-related activities it supports in Uganda. The catchment has undergone tremendous human-induced land use/cover changes, which have not been quantified. This study aimed at quantifying the land use/cover changes as well as the rate at which these changes occurred over the last three decades in the catchment. This was achieved using remote sensing techniques and Geographic Information System (GIS) to analyse and contextualize the changes. To that effect, images of Landsat satellites MSS, TM, ETM+ and OLI were interpreted using supervised image classification technique to determine the land use/land cover changes from 1984 to 2015. The obtained results indicated that the catchment has undergone huge land use and land cover transformations over the last three decades attributable to rapid population growth and urbanization. The prevailing changes in footprint between 1984 and 2015 were expansions of built–up land (20.58% to 49.59%) and open water bodies (not detected in 1984 to 1.74%), and decreases in the following sectors: agricultural lands (from 43.88% to 26.10%), forestland (from 23.78% to 17.49%), and wetlands (from 11.76% to 5.08%). The changes pose a threat to the environment and water quality of the Murchison Bay and consequently increases National Water and Sewerage Corporation water treatment costs. Therefore, there is the need to take critical and practical measures to regulate and police land use, water use rights and conserve the environment especially wetlands.
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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 | 0.000 |
| 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 it