Gray Wolf, Canis lupus lycaon, responses to shifts of White-tailed Deer, Odocoileus virginianus, adjacent to Algonquin Provincial Park, Ontario
Bibliographic record
Abstract
Odocoileus virginianus, adjacent to Algonquin Provincial Park, Ontario.Canadian Field-Naturalist 114(1): 62-71.Changes in the distribution of White-tailed Deer (Odocoileus virginianus) affected the distribution of Gray Wolves (Canis lupus lycaon) within and adjacent to the Round Lake deer yard in central Ontario.This prompted us to re-examine the antipredator benefits of yarding for deer.The distribution of deer and wolves in the study area, and locations of deer kills were documented in four periods over the winter.Road and forest deer track counts, infrared monitoring at deer migration routes, and wolf radio tracking techniques were the main methods employed.As snow depth increased, deer distribution changed from a loose aggregation in late January to a more clumped distribution in March.Three wolf packs had their territories partially or fully within the deer yard and 13 radio-collared migratory wolves and their pack mates in seven packs, left their territories in the park and followed the deer to the concentration area, resulting in a high density of wolves during the winter.The majority of wolves responded to the shifting deer distribution by staying in areas of high deer density within the yard and its periphery, and 21 of 27 kill-sites found were located in these areas.The loose aggregation of mostly small deer groups in the Round Lake deer yard meant that enhanced predator detection as well as increased confusion of the predator during an attack presumably were minor factors contributing to greater safety in the yard.As a result of the aggregation of migratory and resident wolves in the yard, the ratio of predator to prey was not lowered as it may be in other cases.Except for the possibility of enhanced escape using the trail network, there appears to be little evidence for antipredator benefits of yarding for deer in the Round Lake deer yard.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".