Bibliographic record
Abstract
Carbon emissions from tropical deforestation have long been recognized as a key component of the global carbon budget, and more recently of our global climate system. Tropical forest clearing accounts for roughly 20% of anthropogenic carbon emissions and destroys globally significant carbon sinks (IPCC 2007 ). Global climate policy initiatives are now being proposed to address these emissions and to more actively include developing countries in greenhouse gas mitigation (e.g. Santilli et al 2005 , Gullison et al 2007 ). In 2005, at the Conference of the Parties (COP) in Montreal, the United Nations Framework Convention on Climate Change (UNFCCC) launched a new initiative to assess the scientific and technical methods and issues for developing policy approaches and incentives to reduce emissions from deforestation and degradation (REDD) in developing countries (Gullison et al 2007 ). Over the last two years the methods and tools needed to estimate reductions in greenhouse gas emissions from deforestation have quickly evolved, as the scientific community responded to the UNFCCC policy needs. This focus issue highlights those advancements, covering some of the most important technical issues for measuring and monitoring emissions from deforestation and forest degradation and emphasizing immediately available methods and data, as well as future challenges. Elements for effective long-term implementation of a REDD mechanism related to both environmental and political concerns are discussed in Mollicone et al . Herold and Johns synthesize viewpoints of national parties to the UNFCCC on REDD and expand upon key issues for linking policy requirements and forest monitoring capabilities. In response to these expressed policy needs, they discuss a remote-sensing-based observation framework to start REDD implementation activities and build historical deforestation databases on the national level. Achard et al offer an assessment of remote sensing measurements across the world's tropical forests that can provide key consistency and prioritization for national-level efforts. Gibbs et al calculate a range of national-level forest carbon stock estimates that can be used immediately, and also review ground-based and remote sensing approaches to estimate national-level tropical carbon stocks with increased accuracy. These papers help illustrate that methodologies and tools are indeed available to estimate emissions from deforestation. Clearly, important technical challenges remain (e.g. quantifying degradation, assessing uncertainty, verification procedures, capacity building, and Landsat data continuity) but we now have a sufficient technical base to support REDD early actions and readiness mechanisms for building national monitoring systems. Thus, we enter the COP 13 in Bali, Indonesia with great hope for a more inclusive climate policy encompassing all countries and emissions sources from both land-use and energy sectors. Our understanding of tropical deforestation and carbon emissions is improving and with that, opportunities to conserve tropical forests and the host of ecosystem services they provide while also increasing revenue streams in developing countries through economic incentives to avoid deforestation and degradation. References Gullison R E et al 2007 Tropical forests and climate policy Science 316 985–6 Intergovernmental Panel on Climate Change (IPCC) 2007 Climate Change 2007: The Physical Science Basis: Summary for Policymakers http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-spm.pdf Santilli M et al 2005 Tropical deforestation and the Kyoto Protocol: an editorial essay Clim. Change 71 267–76 Focus on Tropical Deforestation and Greenhouse Gas Emissions Contents Pan-tropical monitoring of deforestation F Achard, R DeFries, H Eva, M Hansen, P Mayaux and H-J Stibig Monitoring and estimating tropical forest carbon stocks: making REDD a reality Holly K Gibbs, Sandra Brown, John O Niles and Jonathan A Foley Elements for the expected mechanisms on 'reduced emissions from deforestation and degradation, REDD' under UNFCCC D Mollicone, A Freibauer, E D Schulze, S Braatz, G Grassi and S Federici Linking requirements with capabilities for deforestation monitoring in the context of the UNFCCC-REDD process Martin Herold and Tracy Johns Reference scenarios for deforestation and forest degradation in support of REDD: a review of data and methods Lydia P Olander, Holly K Gibbs, Marc Steininger, Jennifer J Swenson and Brian C Murray Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates Giacomo Grassi, Suvi Monni, Sandro Federici, Frederic Achard and Danilo Mollicone Identifying optimal areas for REDD intervention: East Kalimantan, Indonesia as a case study Nancy L Harris, Silvia Petrova, Fred Stolle and Sandra Brown A first map of tropical Africa's above-ground biomass derived from satellite imagery A Baccini, N Laporte, S J Goetz, M Sun and H Dong
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".