Emissions from land-cover change in Panama: uncertainty, dynamics, and perceptions
Bibliographic record
Abstract
Land use/cover change (LUCC) associated with tropical deforestation produces 6-17% ofthe total anthropogenic CO2 emissions and is the second largest source of greenhouse gases globally. In Cancun 2010, a policy framework was adopted for the creation of a forest-related climate change mitigation mechanism to Reduce Emissions from Deforestation and forest Degradation in developing countries (REDD+). This mechanism would allow developing countries to be compensated by developed countries for reducing emissions from deforestation or for increasing removals by forests. In the context of REDD+, several methodological issues need to be solved, including better quantification of emissions from LUCC in order estimate credible emission reductions thus ensuring the integrity of the climate regime and the cost-efficiency of a REDD+ mechanism. Using Panama as a case study, the present research improved the understanding of uncertainties associated with the quantification of emissions from land-cover change based on amodeling approach. Forest carbon density is identified as one of the main sources of error. I showed that uncertainties associated with carbon density can affect substantially possible payment a country could receive to reduce its emissions. When performing a full diagnosis of uncertainty, four additional sources were identified including deforestation area, quality of land-cover maps, time interval between two land-cover assessments (snapshot effect) and carbon density of re-growing vegetation. In order to improve information on land-use dynamics and address the uncertainty related to the 'snapshot effect', I developed a new approach using a time series of medium resolution satellite images combined with a field survey of forest carbon stocks to track the impact of intervention over time. This approach provided a good proxy of forest carbon stock change and is a very promising avenue for monitoring dynamic land cover such as shifting cultivation. The methodological aspects of the thesis are complemented by an analysis of forest governance based on the perception of local residents living inside a protected area with ongoing deforestation. Local needs related to food security are identified as possible barrier to REDD+ implementation. The need to establish clear legal rights over access and use of forest resources to balance human needs and forest conservation under collaborative management approach is one of the great challenges that REDD+ will face.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".