Above‐ground carbon assessment in the<scp>K</scp>om‐<scp>M</scp>engamé forest conservation complex,<scp>S</scp>outh<scp>C</scp>ameroon: Exploring the potential of managing forests for biodiversity and carbon
Bibliographic record
Abstract
Abstract Protected areas are important for biodiversity conservation and the maintenance of ecosystem services, including climate regulation through carbon storage. Yet, there is little knowledge of their carbon storage potential. This study assesses the above‐ground carbon stock and the congruence between carbon stock and tree diversity in theKom‐Mengamé forest conservation complex (KMFCC) inSouth‐Cameroon, based on an inventory of trees withDBH≥ 10 cm in 1,366 plots (100 × 5 m each) covering 63.8 ha, established in different land use types (terra firma forest, swamp forest and cultivated areas). Above‐ground carbon was estimated using generic allometric equation and species‐specific wood density derived from wood density databases. Results showed high carbon stock inKMFCCwith values ranging from 143.29 ± 124.37 Mg/ha‐1 in swamp areas to 240 ± 204.35 Mg/ha‐1 in terra firma forests. Mean carbon stock in managed areas differed from that of terra firma forests. Petersianthus macrocarpus showed the greatest carbon stock. The study demonstrates the need for integrated approaches for carbon management in secondary forests where agroforests might be important to maintain biodiversity associated with high carbon storage. These approaches are particularly relevant to theCongo basin region where protected areas are threatened by poor management of their periphery.
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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".