Mapping the land cover dynamics of the Moanda Mangrove Marine Park in Central Kongo Province, DRC from 2002 to 2020
Bibliographic record
Abstract
The mangrove land cover is undergoing unprecedented anthropization. The main objective of this study is to map and quantify the dynamics of land cover in the Moanda Mangrove Marine Park between 2002 and 2020. Specifically, it aims to: (i) assess the annual rate of change in the mangrove land cover; (ii) estimate the influence of wood energy on the decline in mangrove forest area; and (iii) propose measures for the sustainable management of the Mangrove Marine Park forest. The study method is a combination of field survey, diachronic analysis of Landsat images and statistical analysis. The very good correspondence between the classification results and field reality was justified by the Kappa value (0.81) and overall accuracy (82.4%). The diachronic analysis of satellite images showed the regression of areas covered by forest classes in favor of the anthropogenic activities, herbaceous mangroves and the savannah. The annual deforestation rate in the Moanda Mangrove Marine Park is estimated at around 0.07%. The result points to the heavy dependence of coastal households in Moanda on fishing (31%), agriculture (26%) and charcoal production (22%), all of which put pressure on the natural resources of the Mangrove Marine Park. Promoting an integrated approach and techniques for the sustainable use of natural resources is an effective way of combating deforestation in the Moanda Mangrove Marine Park.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".