Derivation of Coastal Erosion Susceptibility and Socio-Economic Vulnerability Models for Sustainable Coastal Management in Senegal
Bibliographic record
Abstract
Coastal erosion has posed significant challenges to sustainability and socio-economic stability along Senegal’s coastline, leading to substantial infrastructure losses. Using GIS multi-criteria decision analysis (MCDA), two sub-indices were derived for Senegal’s coastal departments: the physical susceptibility (PSI) and the social-economic vulnerability (SVI) to coastal erosion. The integrated coastal erosion vulnerability (ICER) model was derived by their aggregation. A total of 26 criteria were used, 18 for PSI and 8 for SVI. The criteria weighting coefficients of the sub-indices were determined using the analytic hierarchy process (AHP). Validation of the model accuracy was performed using receiver operating characteristic (ROC) curves that were calculated based on a created coastal erosion cadaster and true positive (TP) sites and manually acquired true negative (TN) sites. The accuracy assessment confirmed the consistency of the physical susceptibility model (PSI) and proved that existing coastal erosion sites are within (5) very high susceptibility areas. Through the generated ICER, the coastal departments were divided into areas of (1) very low, (2) low, (3) medium, (4) high and (5) very high vulnerability to coastal erosion. Very high (5) and high (4) classes cover around 31% of the coastal departments, mostly encompassing a narrow coastal strip and low river valleys and mouths. The presented coastal susceptibility and vulnerability maps, with a spatial resolution of 30 m, identified problematic areas in Senegal’s coastal departments and can help decision-makers in the construction of effective coastal zone management and sustainable development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".