Tawny owl prey remains indicate differences in the dynamics of coastal and inland vole populations in southern Finland
Bibliographic record
Abstract
Abstract Some studies suggest that mild winters decrease overwinter survival of small mammals or coincide with decreased cyclicity in vole numbers, whereas other studies suggest non‐significant or positive relationships between mild winter conditions and vole population dynamics. We expect for the number of voles to be higher in the rich and low‐lying habitats of the coastal areas than in the less fertile areas inland. We assume that this geographical difference in vole abundances is diminished by mild winters especially in low‐lying habitats. We examine these relationships by generalized linear mixed models using prey remains of breeding tawny owls Strix aluco as a proxy for the abundance of voles. The higher number of small voles in the coastal area than in the inland area suggest that vole populations were denser in the coastal area. Vole populations of both areas were affected by winter weather conditions particularly in March, but these relationships differed between areas. The mild ends of winter with frequent fluctuations of the ambient temperature around the freezing point (“frost seesaw”) constrained significantly the coastal vole populations, while deep snow cover, in general after hard winters, was followed by significantly lowered number of voles only in the inland populations. Our results suggest that coastal vole populations are more vulnerable to mild winters than inland ones. We also show that tawny owl prey remains can be used in a meaningful way to study vole population dynamics.
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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 | 0.001 |
| 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) | 0.001 | 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".