Red squirrels and predation risk to bird nests in northern forests
Bibliographic record
Abstract
Red squirrels (Tamiasciurus hudsonicus) are important predators on bird nests in northern conifer forests, and previous work has shown that nest density of understory birds is low in these forests compared with deciduous forest. Here, we examine the relationships between the risk of squirrel predation and nest distribution at a smaller, within-habitat scale using both experimental and comparative studies. Female squirrels depredated experimental nests more quickly than males in interior forests near the Yukon British Columbia border, but after 2 weeks, there was no difference in the percentage of nests depredated by males and females. The density of squirrels and the risk of experimental nest predation increased but the index of natural nest density did not decrease with the density of cone-bearing Sitka spruce (Picea sitchensis) trees in coastal conifer forests of Southeast Alaska. Experimental nests in successional deciduous stands had high risks of predation, in part because squirrels occupied small stands of colonizing spruces in the deciduous matrix and foraged widely in the deciduous stands. In the experimental study site, natural nests occurred at similar densities both next to and away from squirrel-occupied spruce stands, but in other areas, there was a "halo" of low nest density in deciduous vegetation next to spruce stands. Overall, there was little evidence that, within habitats, birds chose nest sites that minimized the risk of squirrel predation.
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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".