Sustainable Wetland Management Using the Kunming-Montreal Global Biodiversity Framework as a Guide in the Sierra Leone Case
Bibliographic record
Abstract
The Sustainable Wetland Management adopted for this study depicts that, the identification of drivers and impacts is needed first, in other to get a clearer roadmap, after which the Kunming-Montreal Global Biodiversity Framework would come into play to serve as a pathway for Sustainability. The study evaluates how Sierra Leone might implement the Framework’s proposed strategies in National Wetland Management. As a result, the research tried to thoroughly examine the factors that contribute to wetland degradation as well as the effects they have on the people who live nearby. The purposive sampling method was used to administer 385 structured questionnaires to inhabitants. The data was then processed in an Excel spreadsheet. Microsoft Publisher was used to draw the framework and a descriptive analysis was done. Results indicated that; the majority of the inhabitants of Aberdeen Creek are traders/self-employed, furthermore, the majority chose the place because it’s less expensive and nearer to the workplace, settlement expansion and pollution are the two most common degrading activities, while flooding and health-related issues are some of the consequences, and the Kunming-Montreal Global Biodiversity Framework is regarded to be a perfect tool for wetland management. It was concluded that to accomplish the objectives in the framework, it is necessary to have both political and social will. Satellite data and water quality research are further needed to validate the report.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".