Local Mangrove Planting in the Philippines: Are Fisherfolk and Fishpond Owners Effective Restorationists?
Bibliographic record
Abstract
Abstract Local fisherfolk and fishpond owners have been practicing “restoration” of mangrove forests in some parts of the Philippines for decades, well before governments and non‐government organizations began to promote the activity as a conservation tool. This paper examines ecological characteristics of these mangrove plantations and compares them to natural mangroves in the same areas. Mangrove planters were interviewed and plantation and natural mangrove forests were surveyed to measure forest structure, composition and regeneration. Compared with natural forests, mangrove plantations were characterized by high densities of small stems, shorter and narrower canopies, and fewer species. For both economic and ecological reasons, the vast majority of people dispersed and planted only Rhizophora mucronata/stylosa and, furthermore, they often thinned other species out of planted areas. There was remarkably little subsequent recruitment of other, nonplanted mangrove species into plantations up to 50 and 60 years of age. This pattern held across a diversity of sites, including plantations that had not been selectively cut or weeded. Important ecological and economic benefits result from local mangrove planting, but catalyzing diverse forest regeneration—at least in the short to medium term—is not one of them. The lesson: if you want to restore diverse mangrove forests, you have to plant diverse mangrove forests.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".