Central African forests, carbon and climate change
Bibliographic record
Abstract
CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 17:229-246 (2001) - doi:10.3354/cr017229 Central African forests, carbon and climate change Chris Justice1,*, David Wilkie2,**, Quanfa Zhang1, Jake Brunner3, Cinde Donoghue1,*** 1Global Environmental Change Program, Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia 22903, USA 2Associates in Forest Research and Development, Waltham, Massachusetts 02154, USA 3World Resources Institute, Washington, DC 20002, USA *E-mail: justice@virginia.edu Present addresses: **Wildlife Conservation Society, Bronx, New York 10460, USA, and Boston College, Chestnut Hill, Massachusetts 02467, USA ***Washington State Department of Ecology, Olympia, Washington 98504-8711, USA 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. KEY WORDS: The Congo Basin · Climate change · Carbon · Forest management and policy Full text in pdf format PreviousExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 17, No. 2. Online publication date: August 15, 2001 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2001 Inter-Research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".