Spatial Changes in the Wetlands of Lagos/Lekki Lagoons of Lagos, Nigeria
Bibliographic record
Abstract
Lagos metropolis, the current economic capital of Nigeria is a low-lying coastal city endowed with a number of lagoons and wetland ecological assets. Lagos/Lekki Lagoons being the largest with a combined size of 646km2 are fringed on many sides by wetlands. Many of these wetlands have undergone severe spatial changes from rapid urbanization in the past three decades. The precise nature of these changes is largely unknown and unreported. As the area is experiencing intense development pressure, this study therefore examined the spatial changes in the wetlands fringing these lagoons using the integrated approach of remote sensing data and GIS with topographic maps providing baseline data. The objective is to quantify and establish the precise location and magnitude of these changes over the years from 1984 to 2006. Two types of wetlands are prevalent in the Lagos area namely: the swamps and mangroves. ENVI software was used along with parallelepiped supervised classification in processing the Landsat images. Results show that the mangrove wetlands decreased from 88.51km2 to 19.95km2 at -3.12km2 annually while swamps decreased from 344.75km2 to 165.37km2 at - 8.15km2 annually both between 1984 and 2006. Results further show that mangroves which were widespread in seven council areas around these lagoons in 1984, have dwindled to only four councils in 2006. These decreases are attributable to urban development pressures. Some of the implications of these losses and conservation issues are briefly highlighted.
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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 | 0.000 |
| 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 | 0.001 | 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".