Wave dynamics and shoreline evolution in deltas: A case study of sandy coasts in the Volta delta of Ghana
Bibliographic record
Abstract
Abstract Erosion is a major global challenge facing coastal regions, and it is projected to increase on a regional to global scale as sea levels continue to rise. Deltas, which are important ecosystems, are particularly vulnerable due to their low-lying nature, subsidence, reduction in sediment supply, population increase, and exposure to an increasing frequency of extreme events. The need for sustainable management of these systems requires accurate estimates of shoreline dynamics at the local scale and higher spatial resolutions for engineering and decision making. We have assessed the shoreline dynamics of the Volta River delta in Ghana for a medium term of 12 years using high-resolution satellite imagery. The shoreline change rates are correlated with wave dynamics to explain the observed shoreline evolution within the delta. Our results confirm that erosion dominates the studied coasts with rates reaching as high as 31 m/yr close to the mouth of the delta where the water level shows a strong relationship with the shoreline change. These rates are evidenced by the destruction of fishing villages such as Fuveme located close to the mouth. Anthropogenic factors, such as the construction of sea defense projects, are also influencing erosion patterns across the study area. We recommend a softer approach for coastal management within the delta.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".