Understanding avian nest predation: why ornithologists should study snakes
Bibliographic record
Abstract
Despite the overriding importance of nest predation for most birds, our understanding of the relationship between birds and their nest predators has been developed largely without reliable information on the identity of the predators. Miniature video cameras placed at nests are changing that situation and in six of eight recent studies of New World passerine birds, snakes were the most important nest predators. Several areas of research stand to gain important insights from understanding more about the snakes that prey on birds' nests. Birds nesting in fragmented habitats often experience increased nest predation. Snakes could be attracted to habitat edges because they are thermally superior habitats, coincidentally increasing predation, or snakes could be attracted directly by greater prey abundance in edges. Birds might reduce predation risk from snakes by nesting in locations inaccessible to snakes or in locations that are thermally inhospitable to snakes, although potentially at some cost to themselves or their young. Nesting birds should also modify their behavior to reduce exposure to visually orienting snakes. Ornithologists incorporating snakes into their ecological or conservation research need to be aware of practical considerations, including sampling difficulties and logistical challenges associated with quantifying snake habitat use.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".