Fundamental but underrepresented: root carbon stocks in African montane forests
Bibliographic record
Abstract
African montane forests harbor some of the greatest biodiversity worldwide, with high levels of species endemism. However, the loss of these forests through fragmentation, deforestation and climate change has been rapidly increasing in recent years. Montane forests in Africa are more susceptible to changes in climate than their lowland counterparts, yet their ecological value is still underrepresented. These montane forests have recently been highlighted as a major aboveground carbon (C) stock. The estimated 149.4 Mg C ha−1 from aboveground live trees surpasses estimates from the Intergovernmental Panel on Climate Change for these endangered forests, and exceeds reported values for neotropical montane and lowland forests by up to 70%. Despite the tremendous implications of these findings, coordinated and available research on the C storage potential of the other share of African montane forest biomass, that is in tree roots, is largely missing. Broadly estimated from the allometry of aboveground C stocks and from root:shoot ratios in lowland forests, more than 60 Mg C ha−1 can be stored in African montane forest roots, about 40% more than previously determined. While this broad estimation points at the potential importance of root C stocks in African montane forests, it also unveils a far-reaching knowledge gap. Here, we advocate for a more quantitative representation of the root C stock from dominant forest tree species of African tropical montane forests and ultimately for a better grasp on tree C stocks from this endangered ecosystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".