Restoration of Woodland in Inhabited Rural Mountainous Areas and Landscape Transformation in a Vulnerable Environment: The Case of the Village of Vallue, Palmes Region, Haiti
Bibliographic record
Abstract
Biodiversity is under threat in Haiti. FAO, in its 2020 report on the state of the world's forests, indicates that forest coverage in Haiti represents 12.6% of its territory; thus, this lack in wooded areas increases its vulnerability to natural disasters. To try recovering in losses of forest coverage and biodiversity at the local level, the organization of farmers in Vallue a rural area of Petit-Goâve (a city in the western department), has mobilized its members, taped to its resources and embarked into reforestation projects while helping to improve habitat in the village. This research analyzes the relationship among biodiversity resources in reforestation projects involving native and exotic species, habitat and landscape transformation in an environment that is vulnerable to natural disasters with the goal to seek for solutions that can benefit rural communities. A field investigation is conducted with 156 families who live in the village and a series of satellite images are analyzed to evaluate the percentage of forest coverage. The results obtained indicate habitats are destroyed by disasters that regularly hit the region; reforestation projects that include exotic species do not have adverse effects on native ones and wooded areas are improved in the village of Vallue. Furthermore, the landscape retains a visual quality that can facilitate the valorization of the territory. It is proposed that reforestation projects with plants that resist to high winds to be used and develop a bio-reactive habitat that may reduce the vulnerability of rural families living in areas too often struck by hurricanes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".