"Mapping Carbon Stock from Landcover Dynamics within the Southern Slope of Mount Bamboutos, Cameroon"
Bibliographic record
Abstract
The southern slope of Mount Bamboutos, Cameroon, a critical tropical montane ecosystem, faces escalating landcover degradation, threatening carbon stocks and biodiversity.This study integrates remote sensing (Landsat/SPOT imagery: 1992-2023) with dendrometric inventories from 30 plots (20mx20m) to quantify landcover changes and associated carbon stock variations.Results show a 23.62% decline in dense montane forest over three decades, with modified agroforestry and grassland/cropland expanding by 28.04% and 56.50%, respectively.Agroforestry systems dominated by Eucalyptus grandis and Persea americana exhibited the highest aboveground carbon density (2.15kg/m²), despite lower biodiversity (Shannon H′=1.70).Dense forests demonstrated the greatest species richness (H′=3.67)but lower carbon density (0.38kg/m²), while grassland/cropland stored minimal carbon (0.20kg/m²).Tree structural parameters (DBH, height, density) strongly predicted carbon stock (R=0.738,p<0.001).Projections suggest cropland/grassland may exceed 50% coverage by 2050, further reducing carbon sequestration capacity.The findings underscore the need for integrated REDD+initiatives, forest restoration, and sustainable agroforestry practices to safeguard ecosystem services.Spatially explicit carbon maps and targeted strategies are proposed to inform climate mitigation policies in Cameroon's montane landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".