DISPERSED TREES IN PASTURELANDS OF CATTLE FARMS IN A TROPICAL DRY ECOSYSTEM
Bibliographic record
Abstract
In many tropical cattle farms of Central America, farmers commonly retain trees in pastures to obtain timber and provide shade and fodder to cattle. However, little is known about the diversity, abundance, richness and species composition of dispersed trees in pastures of cattle farms in the dry tropics. Therefore, the objective of this study was to characterize and describe the pattern of tree cover dispersed in pastures of cattle farm systems assessing their roles in sustaining farm productivity. The study was conducted in 16 cattle farms in a tropical dry ecosystem in Costa Rica. A total of 5,896 trees, from 36 families and 99 species, were found dispersed in pastures (836 ha). Trees were present on 100% of the farms and in 85% of pastures and they occurred as individual trees (54%) and clustered (46%). The most abundant families are Bignonaceae, Sterculeaceae and Boraginaceae. The most common tree species were Tabebuia rosea (Bertol.) DC, Guazuma ulmifolia Lam, Cordia alliodora (Ruiz & Pav.) Oken and Acrocomia aculeata (Jacq.) Lodd. ex Mart, which together accounted for 60% of the total number of trees. Tree species with smaller crowns are found at higher densities than tree species with large crowns. Pastures mean crown cover was 7% (SE + 0.54) and mean tree density was 8.1 trees ha-1 (SE + 0.66). We conclude that farmers are managing a low tree diversity, cover (m2 ha-1) and density (trees ha-1) for fulfilling different farm needs that contribute to farm productivity but minimizing interference with pasture productivity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".