Spatial Mapping of Carbon Stock in Riverine Mangroves Along Amanzule River in the Ellembelle District of Ghana
Bibliographic record
Abstract
Compared to other wetland ecosystems mangroves are well known for their numerous ecosystem services, especially carbon pool. In Ghana, there is limited information on the sequestered carbon in mangroves. There is increasing interest on national climate change mitigation and adaptation plans in mangroves in developing nations, and Ellembelle in the Western Region of Ghana is of no exception. Ellembelle is one of the areas with little information on the size and variation of mangrove carbon stock which needs to be addressed. This research is aimed at determining the carbon stock from the carbon sequestered in mangrove and the areal extent in mangrove forest using remote sensing and allometric equation. The ecosystem carbon density estimate for the mangrove forest was weighted based on their spatial distribution across the landscape to yield a total carbon stock of for the Ellembelle mangrove forest. The error obtained from the 95% Confidence Interval was + 1.53%, which is within the acceptable levels of uncertainty based on the Monte Carlo Analysis. The overall carbon estimated for 2015 based on the area for mangrove (374.49 ha) was 1.550Mt with an uncertainty of +57.125Kt indicating a high amount of carbon sequestered in mangroves.
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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) | 0.001 | 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".