Factors affecting carcass use by a guild of scavengers in European temperate woodland
Bibliographic record
Abstract
Although facultative scavenging is very common, little is known about the factors governing carrion acquisition by vertebrates. We examined the influence of carcass characteristics, carcass state, and weather conditions on carrion use by main scavengers. Carcasses (N = 214, mainly ungulates) of various origins (predation, natural deaths, harvest) were monitored by systematic inspections (N = 1784) in Białowieża Forest (Poland). Common raven (Corvus corax L., 1758), red fox (Vulpes vulpes (L., 1758)), and European pine marten (Martes martes (L., 1758)) mainly used the prey remains of gray wolves (Canis lupus L., 1758). The kills of predators were the preferred carrion, rather than dead ungulates. Common ravens, common buzzards (Buteo buteo (L., 1758)), white-tailed eagles (Haliaeetus albicilla (L., 1758)), and domestic dogs scavenged more frequently on carcasses in open habitats. Carcasses located in the forest were the most available to European pine martens, jays (Garrulus glandarius (L., 1758)), and wild boar (Sus scrofa L., 1758). The common tendency was to increase scavenging when temperature decreased, except for raccoon dogs (Nyctereutes procyonoides (Gray 1834)). As snow depth increased, jays and great tits (Parus major L., 1758) increased scavenging. We suggest that carrion use by scavengers is not random, but a complex process mediated by extrinsic factors and by behavioural adaptations of scavengers.
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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 | 0.000 |
| 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".