From Local Knowledge to Global Goals: Restoring Mangroves in Colombia
Bibliographic record
Abstract
The development of mangrove restoration projects in the Ciénaga Grande de Santa Marta (CGSM), the most important coastal lagoon in Colombia, has demonstrated the effectiveness of a participatory and socio-ecological approach. The success of integrating scientific knowledge with the traditional knowledge of local communities has led to positive and sustainable results for the recovery of this important ecosystem.This restoration process was based on a comprehensive diagnosis that identified the main factors contributing to mangrove degradation (e.g. hypersalinization, sedimentation, canal blockage, loss of consolidated soil and reduction in natural regeneration). The restoration involved local communities, who not only contributed their traditional knowledge about mangroves, but also played a key role in decision making and implementation. A hydrological rehabilitation strategy was implemented that included the manual cleaning of 3.5 km of the main channel and the opening of eight secondary channels to facilitate the flow of fresh water; and the construction of 400 sediment piles to create optimal conditions for natural regeneration.This experience in the CGSM highlights the importance of integrating the SDGs and the Kunming-Montreal Global Biodiversity Framework into mangrove restoration projects. Through integrated mangrove management, progress is being made toward the goals of this framework by strengthening the participation of local communities, ensuring the sustainability of ecosystems, and improving the quality of life of communities. In particular, mangrove restoration contributes to Target 2 of the GBF, which aims to ensure that at least 30% of terrestrial and marine areas, including coastal areas, are effectively conserved and managed.The CGSM experience provides a model for mangrove restoration elsewhere in Colombia and around the world, demonstrating that collaboration between science, local communities, and government can lead to successful projects that contribute to biodiversity conservation and the achievement of the SDGs. This is a valuable contribution to global efforts to restore and manage coastal and marine ecosystems and their services, and encourages diverse perspectives, including transdisciplinary approaches, to advance sustainable and equitable management practices.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 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".