Natal habitat-biased dispersal in the Siberian flying squirrel
Bibliographic record
Abstract
Theoretically, dispersers should target the habitat where prospects for fitness will be highest. Aiming for a habitat similar to the natal area (natal habitat-biased dispersal) has been hypothesized as a probable rule of thumb for dispersers, but has received very little empirical support to date. We investigated similarities between natal and post-dispersal settlement sites with radio-collared Siberian flying squirrels (Pteromys volans L.). Juveniles born in small patches and raised in nests close to patch edge settled in small patches and used nests close to edges after dispersal. In addition, post-dispersal use of dreys (versus cavities) was similar to that observed in natal sites. However, the quality of settlement habitat was unrelated to the quality of the natal site, which suggests that natal experience on average-quality habitats may not lead to ecological traps for flying squirrels. This study provides evidence that habitat selection at the landscape scale is influenced by habitat of natal area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".