Moose winter browsing affects the breeding success of great tits
Bibliographic record
Abstract
In many areas, ungulates may have a large impact on the landscape due to their large body size and wide distribution. Moose (Alces alces) winter feeding has been carried out for a decade in parts of Hedmark County, southeast Norway. Previous studies have documented a gradual decline in browsing pressure away from the feeding stations. We utilized this gradient to study the indirect effects of moose browsing on a passerine bird, the great tit (Parus major). The downy birch (Betula pubescens) trees surrounding the feeding stations are subject to an intense browsing pressure gradually decreasing outwards. We put up nest boxes at feeding stations (i.e., sites of intense browsing pressure), with nearby controls with low visible browsing. The number of tall birch trees and the birch canopy cover was lower in browsed compared to control plots. Due to the reduced birch canopy cover, the total biomass of arthropods available around the nest boxes was lower in browsed compared to control plots. Great tit breeding pairs produced 1.3 fewer fledglings in browsed compared to control plots. We suggest this difference to be caused by lack of food in the browsed plots. Hence, moose browsing may reduce the breeding success of great tits through a series of knock-on effects through two other components of the boreal forest community.
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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 | 0.000 |
| 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".