Drone as a tool for coastal flood monitoring in the Volta Delta, Ghana
Why this work is in the frame
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Bibliographic record
Abstract
Monitoring coastal erosion and flooding in deltaic environment is a major challenge. The uncertainties associated with land based methods and remote sensing approaches affect the levels of accuracy, reliability and usability of the output maps generated. This study monitored flooding and erosion activities in a flood prone fishing community (Fuvemeh) in the Volta Delta in Ghana using Unmanned Aerial Vehicles (UAVs) or drone technology. The study revealed that coastal flooding and coastal erosion have destroyed sources of livelihood and increased risk to life and property in the Volta Delta communities. It was identified that between 2005 and 2017 the shoreline has moved several meters inland (over 100 m along some transects) in some areas, while in other areas about 24,057 m 2 land has been gained (about 80 m along some transects) that can serve as natural fish landing site. It emerged that over 77 houses have been destroyed which resulted in the displacement of over 300 inhabitants between 2005 and 2017. The study estimated that about 37% of the total land area in Fuvemeh has been lost as a result of erosion. Coastal erosion and flooding are major environmental challenges in the Volta delta. Coastal erosion has destroyed natural fish landing sites, which has affected the local fishing business (the main source of livelihood) and increased poverty. Coastal flooding has displaced inhabitants from their homes and increased migration from the Fuvemeh community.
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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 | 0.000 | 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 it