Central African forests, carbon and climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The tropical forests of the world are receiving considerable attention in terms of their role in climate change. Not only does tropical land use change provide an important term in balancing the global carbon budget, but tropical forests also present opportunities for carbon trading in the emerging carbon markets. The Congo Basin contains the second largest area of contiguous rainforest in the world, yet for various reasons has received relatively little attention in terms of these climate change issues. This paper provides an assessment of the current state of the forests of Central Africa, their carbon stock, recent rates of deforestation and a simple predictive model of forest change over the next 60 yr. The roles of agriculture and logging which are driving deforestation are discussed. The future of the forests, whether for commercial use, carbon trading or biodiversity is inextricably linked to how these valuable resources are managed. Suggestions are made for potential carbon trading projects, forest management strategies and a climate change research agenda for the region. Effective forest monitoring and management are seen as essential components for the economic development of this region.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it