A West African coastal science trajectory of vulnerability, adaptability, and resilience
Bibliographic record
Abstract
The West African coast faces intensifying vulnerabilities due to climate change, rapid urbanization, and anthropogenic pressures that threaten ecosystems, livelihoods, and infrastructure. To understand these vulnerabilities and be able to decipher solutions in West Africa, a comprehensive strategy is required. Using biannual WACA-VAR workshops between 2018 and 2024, and SCOPUS analysis over the 1998–2024 period, the VOSviewer tool was used to visualize coastal vulnerability to coastal erosion, flooding, pollution, and mangrove degradation across 14 coastal nations. The paper highlights hotspots like Senegal’s Saint-Louis, Ghana’s Volta Delta, and Nigeria’s Niger Delta. These hotspots regulate the evolution of key huge ecosystems within the subregion. The study revealed a comprehensive framework categorizing the factors, expertise, tools, and challenges associated with coastal development and management in West Africa as well as data limitations, and progress made towards addressing these vulnerabilities. The study shows remote sensing tools could be prioritized to investigate nature-based solutions. Nigeria, Ghana, Ivory Coast, Senegal, and Mauritania experience the largest vulnerability based on combined mangrove cover, water quality, and coastal hazards indices. Senegal records the highest average erosion per year, while Nigeria records the fastest decline in mangrove cover. The emergence and development of new research dynamics over the last decade within the region show some progress made to address these endeavours, yet the challenges outperform this progress. A comprehensive data management strategy focused on emerging thematic areas of research has been proposed to address large-scale and small-scale hotspots of erosion and flooding identified within this subregion. The study proposes a framework where academic, industrial, and governmental projects must be harmonized. The implementation of common standardized tools and methodologies will enhance data collection and management. It is imperative that teams, individuals, organisations, and their efforts deployed in terms of hotspot management focus on bridging existing knowledge gaps. Thematic expert teams should be provided with actionable guidelines and methodologies for the implementation of strategies at these identified hotspots.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".