Bacurú Drõa: Indigenous forest custody as an effective climate change mitigation option. A case study from Darién, Panama
Bibliographic record
Abstract
Efforts to naturally remove atmospheric CO 2 demand that largely intact forests be maintained. Our inter-cultural research initiative tested the hypothesis that Indigenous custody of the land is compatible with the maintenance of intact forests. Here we combined traditional knowledge, phytolith analysis, remote sensing, and tree inventories to study old-growth forests in Panama's Darién. Phytoliths served to elucidate historical vegetation, remote sensing revealed the current and past Indigenous footprints while tree stature and identity characterised the forest. Until now there has been very little to no human impact within these forests and current Indigenous footprint is both small and stable. Large trees accounted for 13% of trees in the plots that we established. For over half of the species, the measured tree height was taller than previously published maximum heights, leading us to conclude that these forests are a truly exceptional ecological refugium. Noting that the local communities are not rewarded for their custody of these exceptional forests we call to revisit the Good Practice Guidance for Land Use Land Use Change and Forestry to include intact forest land. In the context of sub-optimal carbon finance options, we also propose matching as a methodology that could prove additionality of forest conservation initiatives in climate mitigation portfolios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".