Full and effective participation of indigenous peoples in forest monitoring for reducing emissions from deforestation and forest degradation (<scp>REDD</scp>+): trial in Panama's Darién
Bibliographic record
Abstract
Abstract A primary technical requirement of the climate change mitigation mechanism, reducing emissions from deforestation and forest degradation (REDD+), is to calculate emissions factors, that is, the amount of CO2 emissions or removals per hectare from land use and land‐use change. Emissions factors are calculated from baseline estimates of the aboveground biomass (AGB) stored in different vegetation types. Ground‐based methods for estimating AGB, such as forest inventories, despite being relatively accurate and necessary for calibrating remotely sensed data such as satellite or airborne Light Detection and Ranging, tend to be expensive and time‐consuming. Thus, calls have been made to improve the cost‐efficiency of these methods within the context of REDD+. Also as part of REDD+, there have been calls for the legitimate inclusion of indigenous peoples and rural communities in various aspects of the mechanism. To address both of these issues, we devised a participatory, rapid, forest inventorying method and tested it across the heterogeneous forest landscape of Darién, Panama. This effort took place within a project that was administratively and logistically managed entirely by an indigenous organization working in collaboration with indigenous authorities in Darién, with funding from the World Bank. A group of 24 indigenous technicians were trained on forest inventorying methods. They established and measured thirty 1‐ha plots under our direct supervision. We tested for various sources of error in tree diameter and height measurements. We also tested the scalability of our tree‐level biomass estimates to the plot level by comparing our results with simulations conducted on the Barro Colorado Island 50‐ha permanent plot data. Results indicate that our rapid, participatory, forest inventorying method effectively captures plot‐level AGB, while guaranteeing the full and effective participation of indigenous peoples. The benefits of our method in terms of cost‐efficiency and access to remote forest areas are discussed, as well as those accrued by indigenous peoples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".