Mule Deer, White-Tailed Deer, and Wolves in Jasper National Park, Alberta: 35 Years of Sightings, 1981–2016
Bibliographic record
Abstract
On the basis of annual observations collected over 35 y, we chronicled the trends in abundance of Mule Deer (Odocoileus hemionus) and White-tailed Deer (Odocoileus virginianus) in semi-open montane habitat in the Devona district of Jasper National Park (JNP), Alberta, 1981–2016. During 722 d of observations conducted in winter over this period, we recorded a decline in Mule Deer and the incursion of White-tailed Deer into the park. Of a total of 429 deer sighted, White-tailed Deer increased from an average of 0.08 sightings/d to 0.73/d, whereas the native Mule Deer declined from 0.42 sightings/d to 0.01/d. Over the same time span, sightings of all deer increased from 0.51/d to 0.74/d. Although the ultimate cause of the opposing population trends of the 2 deer species is not certain, we review the proximate causes discussed in relevant literature, and we compare the results of our census to a list of deer killed by vehicle collisions on JNP roads and highways. As a measure of the presence of Gray Wolves (Canis lupus) in the study area, we recorded the largest size of wolf packs sighted each year and found no trend over time. The question of whether wolf predation on the 2 deer species could account for their opposing population trends remains to be investigated.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".