Vines and canopy contact: a route for snake predation on parrot nests
Bibliographic record
Abstract
Ornithologists have hypothesized that some tropical forest birds avoid snake predation by nesting in isolated trees that do not have vines and canopy contact with neighbouring trees. Here we review two complementary studies that support this hypothesis by demonstrating (1) that an abundance of vines and an interlocking canopy characterized Jamaican Black-billed Parrot Amazona agilis nest-trees that failed due to chick loss, presumably to snakes, and (2) that such trees were used preferentially by an arboreal snake congeneric to the snake implicated in the parrot losses. Evidence strongly suggested that losses of nestling Black-billed Parrots were due in substantial part to predation by the Jamaican (yellow) Boa Epicrates subflavus (Boidae). Studies of the closely related Epicrates inornatus on Puerto Rico revealed that trees used by boas had more vine cover and more crown or canopy contact with neighbouring trees than did randomly selected trees. Moreover, the boas had relatively large home ranges and were most active during March to July, which corresponds with the breeding season of parrots as well as other bird species. We suggest that nest failure due to snakes may be reduced in endangered bird species through isolating the nest-tree by eliminating vines and canopy contact with neighbouring trees and shrubs and by placing barriers on the nest-tree trunk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".