Achieving mangrove conservation and sustainable use in Mexico through community-based Management Units for Wildlife Conservation within and beyond Protected Areas
Bibliographic record
Abstract
Mangroves are valuable socio-ecological ecosystems that provide vital goods and services to millions of people, including wood, a renewable natural capital, which is the primary source of energy and construction material for several coastal communities in developing countries. Unfortunately, mangrove loss and degradation occur at alarming rates. Regardless of the protection and close monitoring of mangrove ecosystems in Mexico during the last two decades, mangrove degradation and the loss of biodiversity is still ongoing. In some regions, unregulated and unsustainable mangrove wood harvesting are important causes of degradation. In this context, community-based mangrove forestry through Management Units for Wildlife Conservation could be a cost-effective alternative scheme to manage and conserve mangrove forests, their ecosystem services and biological diversity within and beyond protected areas while providing sustainable local livelihoods and helping reduce illegal logging. The objective of the Management Units is to promote alternative means of production with the rational and planned use of renewable resources based on Management Plans. If implemented with a multidisciplinary perspective that incorporates scientific assessments this conservation strategy may contribute to achieving national and international environmental and biodiversity agreements providing multiple social, ecological and economic benefits from local to global scales.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".