Barking up the wrong tree: climbing performance of rat snakes and its implications for depredation of avian nests
Bibliographic record
Abstract
Nest depredation is the leading cause of nest failure in NeotropicalNearctic migratory birds, which are of interest because of their declining populations. In a recent study in a bottomland hardwood forest, Acadian Flycatchers (Empidonax virescens) experienced higher nest success in Nuttall oak (Quercus nuttallii), a tree species with relatively smooth bark at maturity. To determine if variation in bark-surface irregularities may influence the ability of a predator species to access the contents of avian nests, we examined the climbing abilities of rat snakes (Elaphe obsoleta) on trees having three different bark types. None of the subjects was able to ascend large Nuttall oaks in the absence of vines; with vines present, subjects still required more time to climb Nuttall oaks than to climb other species. A few of the subjects successfully climbed smaller Nuttall oaks lacking vines, but ascent time was longer and climbing behavior was modified from that observed in the other trials. Our results indicate that the likelihood of nest predation by rat snakes decreases in this forest when birds nest in trees with smooth bark and without vines. Investigators need to consider differences among nest substrates that are important to both the prey and the predator.
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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 | 0.000 | 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".