Demographic Patterns and Limitation of Grey Wolves, <em>Canis lupus</em>, in and Near Pukaskwa National Park, Ontario
Bibliographic record
Abstract
In response to concern regarding the growth and long-term viability of the wolf population in and near Pukaskwa National Park, a study of demographic patterns and limitation of radio-collared wolves (Canis lupus) was completed between 1994 and 1998. The mean annual finite rate of increase (0.96) suggested that population growth of wolves was limited and declining slightly. Small pack sizes, high cumulative mortality, and low reproductive success also suggested a declining population. Two limiting factors, ungulate biomass and human-caused mortality, were examined to determine the importance of each in limiting the population growth of wolves. Ungulate biomass was involved because occurrence of natural-caused mortality was high (9 of 17 wolves) compared with other studies. In addition, consumption rates were low and similar to other studies where starvation and other signs of malnutrition were noted. Further, Moose densities in the study area were low to moderate and below thresholds indicating nutritional stress for wolves. Occurrence of human-caused mortality was high (8 of 17 wolves) suggesting that it was also an important limiting factor, particularly given the low availability of ungulate biomass and reproduction noted in this study. Based on present demographic patterns, ungulate biomass, and human-caused mortality, the wolf population likely will remain at present low densities or continue to decline.
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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 | 0.001 |
| 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".