Abundance of Panamanian dry-forest birds along gradients of forest cover at multiple scales
Bibliographic record
Abstract
Abstract: Community structure and species’ abundances may be strongly correlated to patterns of forest cover, although such patterns are poorly known for tropical dry-forest birds, especially for those in Panamanian dry forests. Birds were distance-sampled during point counts in five dry-forest fragments in Panama. Distance from point count to forest edge and forest coverage at three spatial scales (500, 1000 and 2000-m radius) were compared as covariate predictors of the abundance of avian species and guilds. Each covariate was selected in at least two models of species or guild abundance. Abundance patterns were consistent with previously reported habitat associations for only two of seven open-habitat or forest-preferring species that showed forest cover-abundance relationships. Null models best described the abundance of all forest species and the subset of uncommon forest species. Thus many of these species appear insensitive to the forest-cover gradients studied. Total abundance of open-habitat-preferring species increased in dry forests with increasing forest coverage within 500 m, suggesting that the relationship between their abundance and vegetation structure are spatial-scale and habitat dependent. Nectarivores had lower abundance as forest cover within 1000 m increased, supporting previous claims that this group is tolerant of forest edges.
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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 | 0.000 | 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) | 0.001 | 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".