Group composition and cohesion in sympatric white-tailed deer and mule deer
Bibliographic record
Abstract
White-tailed deer (Odocoileus virginianus) have been reported to live in smaller groups than mule deer (Odocoileus hemionus). Group size, however, generally varies with habitat conditions, and no comparison has been made between the social structures of the two species living in the same ecological conditions. I compared the size, composition, and stability of groups formed by sympatric whitetails and mule deer living in a prairie habitat in southern Alberta. Seasonal trends were similar for the two species. Females and fawns usually formed small groups during summer. Larger mixed-sex groups became increasingly common during winter, well after the breeding season. Despite the similar seasonal trend, mule deer were significantly more likely than whitetails to occur in relatively large groups composed of both sexes during winter, and whitetails were more likely to occur in small female groups. Mule deer groups were more stable than whitetail groups, and marked mule deer fawns developed strong associations with other known fawns. Habitat variation was limited and was not related to group size. These results show that the composition and cohesion of whitetail and mule deer groups differ, even when the species live in similar circumstances. The differences in grouping behaviour, larger more cohesive groups formed by mule deer than by whitetails, are consistent with those expected to result from the selection pressure of predation.
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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) | 0.000 | 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 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".