PREY SELECTION AND PREDATION BY WOLVES IN BIAŁOWIEŻA PRIMEVAL FOREST, POLAND
Bibliographic record
Abstract
Relationships of wolves (Canis lupus) and ungulates were studied in the Polish part of Białowieża Primeval Forest with high densities of prey. The number of wolves ranged from 7 to 19, and the number of packs ranged from 2 to 4. Average densities were 2.3 wolves/100 km2. Red deer (Cervus elaphus) was the main prey of wolves. Roe deer (Capreolus capreolus), wild boar (Sus scrofa), moose (Alces alces), and European bison (Bison bonasus) were hunted less than expected based on their abundance. Mean mass of ungulates killed by wolves was 55 kg. Prey were consumed quickly, with 57% of kills completely eaten on the 1st day after killing. Average killing rate by wolves was 0.78 ungulate per wolf pack per day (0.14 prey item per wolf per day). Results of this study combined with the data obtained in the Belarussian part of Białowieża Primeval Forest in 1946–1985 allowed for analysis of dietary response of wolves to changes in densities of ungulates. Wolves showed a response to abundance of red deer. The amount of other ungulates in their diet depended on the densities of red deer. From 1991 to 1996, wolves annually removed 57–105 red deer, 19–38 wild boar, 19–25 roe deer, and 0–2 moose per 100 km2. Those amounts were equivalent to 9–13% of spring–summer densities of red deer, 4–8% of wild boar, 3–4% of roe deer, and 0–29% of moose. Additionally, hunters annually harvested 131–140 red deer, 44–114 roe deer, 1–7 moose, and 45–142 wild boar per 100 km2. Effects of predation and harvest by hunters on ungulate mortality were likely additive and caused declines in ungulate populations during our study.
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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 | 0.001 | 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".