Enhancing the sustainable management of mangrove forests: The case of Punta Galeta, Panama
Bibliographic record
Abstract
Mangrove forests fulfill essential socio-ecological roles, such as providing timber and other forest products, protecting coasts against erosion and rising sea levels, supporting healthy fisheries, and fostering biodiversity. Sustainable mangrove management (SMM) aims to address mangrove degradation and reverse trends of mangrove loss while empowering local stakeholders to participate in governance processes. This paper contributes to SMM scholarship through a case study of Punta Galeta, a protected mangrove forest located in the Colón District in Panama, near the Atlantic entrance to the Panama Canal. Our primary objective was to understand the challenges and opportunities associated with SMM in Punta Galeta and to identify insights of relevance to Panama and Latin America. We identified several successful SMM strategies, such as local awareness-raising on the socio-ecological benefits of mangrove forests and corporate sponsorship of mangrove restoration. However, several facets of SMM remained challenging, such as implementing and enforcing management plans and fostering regular communication and collaboration between all stakeholders. Findings suggest that local-level SMM requires a greater focus on strategies to enhance communication, collaboration, and trusting relationships between diverse stakeholders, as well as a more cohesive vision for the sectoral uses of coastlines. Further experimentation with different forms of social organization in support of local sustainable mangrove forest conservation and management are needed.
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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 | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".