Daily movements and territory use by radio-collared wolves (<i>Canis lupus</i>) in Bialowieza Primeval Forest in Poland
Bibliographic record
Abstract
Wolves (Canis lupus) (9 females and 2 males from 4 packs), were radio-tracked in a large Polish woodland in Bialowieza Primeval Forest in 19961999. Based on 360 days of radio tracking with locations taken at 30- or 15-min intervals, daily movement distances (DMDs) of wolves and their utilization of territories were analyzed. DMDs averaged 22.1 km for females and 27.6 km for males. In reproductive and subadult females, DMDs varied seasonally, with the shortest daily routes in May and the longest in autumnwinter. Little seasonal variation was observed in nonbreeding and unsuccessfully breeding adult females. An adult male covered the longest DMDs in February (mating season). The mean speed of travelling wolves was 2.2 km/h. Wolves' hunting activity affected the length and speed of their movements, both of which were higher before than after a kill was made. With growing abundance of prey, DMDs of wolves became shorter. Snow cover and rainfall had a negligible effect on wolf travel. The mean straight-line distance between consecutive daily locations (SLD) was 4.4 km, i.e., on average, 21% of the actual route covered by wolves. Daily ranges utilized by wolves averaged 21.4 km 2 , or 9% of the whole territory. Variation in SLDs and daily ranges was shaped predominantly by mating, breeding, and pup rearing. The pattern of territory use by wolves differed between seasons. In springsummer, their movements concentrated around the breeding den and rendezvous sites, and the areas used on consecutive days overlapped extensively. In autumnwinter, wolves moved widely and utilized their territory in a rotational way, returning to the same parts every 6 days, on average. Rotational use is related to intense patrolling and defense of territory, but may also help wolves to avoid behavioral depression of prey availability.
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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 | 0.000 | 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) | 0.000 | 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".