Insights into the forests of Darién, Panama, from the new 10 ha <i>Bacurú Drõa</i> plot established through participatory methods within an Emberá territory
Bibliographic record
Abstract
Abstract Networks of forest plots are key for documenting how forests are responding to climate change; however, very few plots are in inaccessible locations, and almost no research is carried out in Indigenous territories. We present the first data from a new forest plot co‐developed with the Traditional Emberá Authorities of the Balsa River Collective Lands, Darién, Panama, following a framework of participatory action research: The Bacurú Drõa plot (In Emberá, “ Bacurú ” is tree and “ Drõa” is old, BD). We compare floristic characteristics and conservation status of trees in BD with those of 53 forest plots across Panama. In BD, trees with DBH ≥10 cm were classified in 290 taxonomic units, with 174 (60%) of taxa identified to species, 49 (17%) assigned to genera, and 22 (7.5%) to families, leaving 45 (15.5%) unidentified tree taxa. On a per hectare basis, stem density and species richness differed significantly among plots and groups of plots, both variables being highest in plots located in the Alto Chagres and lowest in the plots located along the Pacific. Estimates of species number for stem density in 1 ha, however, are significantly higher in BD. Conservation value, measured through community weighted mean (CWM) range and CWM International Union for Conservation of Nature (IUCN) score, revealed BD to be of high conservation value when compared to the other ForestGEO plots in Panama. We show that BD has high biodiversity, many singletons, and many unidentified species, consistent with other plots in the Chocó‐Darién Ecoregion, an understudied global biodiversity hotspot. Overall, the Bacurú Drõa plot and surrounding project provide a blueprint on how tropical forest and participatory action research can value and benefit from the contribution of the Indigenous communities that live and conserve the vanishing mature forests of the world while providing sound scientific data.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".