Data on composition and structure of trees inside and outside of forests in Guanacaste, Costa Rica: comparisons among conservation areas, fencerows, and municipal parks
Bibliographic record
Abstract
Many studies of tropical forest ecology occur in protected areas such as national parks. We have a limited understanding of the composition and structure of trees in areas outside forests, such as urban areas or along roads. Here we report three datasets that are critical for understanding how tree communities differ in Guanacaste, Costa Rica. Our data allow the user to contrast trees in three different land-use types that represent a rural to urban gradient: forests inside conservation areas (20 plots), fencerows (68 plots), and municipal parks (36 parks). We measured and identified trees >7cm diameter in addition to collecting ancillary data such as plot area (in hectares) and canopy cover (in percent). We also collated information for each identified species on functional characteristics including: status as native to Guanacaste, Costa Rica, leaf habit (evergreen, deciduous, or other) and whether the tree species produces fruit that are edible for humans. Collectively, these data allow the user to calculate stand-level properties such as basal area and percentage of the tree community that is evergreen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".