Farmed landscapes, trees and forest conservation in Saint Lucia (West Indies)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SUMMARY Islands of the West Indies are among the most historically impacted by agriculture, yet agricultural influences on forests there have been little studied. This research compared tree species richness and vegetation structure between farmed lands, post-agriculture secondary forests and mature remnant forests in two watersheds in Saint Lucia, and sought to understand the current distribution of these habitats in terms of land use and watershed topography. Farms devoted to annual crops had few trees and much exposed soil. By contrast, agroforests had abundant (mostly planted) trees and vegetation structure comparable to secondary forests. Secondary forests had highest overall species richness, but mature forests had the most developed vegetation structure. Variations in habitat distribution reflected different land use histories, with the more rugged west coast long dominated by tree crop farming and the east coast experiencing a recent boom-bust cycle in bananas. Mature and secondary forests were more likely found at higher altitude, further from roads and at sites more difficult to access, the combined result of government protection of key forest and watershed reserves and farmers’ preferential abandonment of marginal lands. For conservationists, this return of forests is reason for optimism and it presents strategic opportunities for public land acquisition or collaborative management to further forest and watershed protection objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it