Are deforestation and degradation in the Congo Basin on the rise? An analysis of recent trends and associated direct drivers.
Bibliographic record
Abstract
Abstract The Congo Basin hosts the largest continuous tract of forest in Africa, regulating global climate while providing essential resources and livelihoods for humans, while harbouring extensive biodiversity. The threats to these forests are expected to increase. A regional collaborative effort has produced the first systematically validated remote sensing assessment of deforestation and degradation in six central African countries for 2015-2020 period, along with a quantification of associated direct drivers of change. Deforestation and degradation (DD) are not observed to be increasing since 2017 are occurring primarily in already fragmented corridor forests. We assess multiple, overlapping drivers and show that the rural complex, a combination of small-scale agriculture, villages, and roads contributes to the majority of DD. Industrial drivers such as mining and forestry are far less common, although their impacts on carbon and biodiversity could be more permanent and significant than informal activities. Artisanal forestry is the only driver that is observed to be consistently increasing over time. Our assessment produces information relevant for climate change mitigation which require detailed information on multiple direct drivers to target activities and investments.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".