Wildlife ecology, conservation, and management
Bibliographic record
Abstract
Professor John Fryxell currently teaches in the Department of Integrative Biology at the University of Guelph, Canada, where he has worked closely with a number of university and government scientists to develop sustainable conservation strategies for elk, woodland caribou, wolves, and marten. Previous to this he worked at the University of British Columbia and as Wildlife Consultant for the Provincial Government of Newfoundland and Labrador. His research has focused on the role of behavior in population and community dynamics of large mammals. He has a continuing interest in African wildlife, including long-term studies on the demography and spatial ecology of large herbivores and their predators in Serengeti National Park. Professor Anthony Sinclair is currently Professor Emeritus at the University of British Columbia, Vancouver, Canada. He has been Director of the Centre for Biodiversity Research at the University, and a Professor at the Department of Zoology. He has researched Canadian subarctic ecosystems and worked on Canadian boreal forest ecosystems, in particular on cycles of snowshoe hares. He worked in the Serengeti National Park, Tanzania, Africa, on ecology and conservation projects for over 40 years. He has conducted ecological research on the Serengeti ecosystem of Tanzania, documenting multiple states in Serengeti savanna and grassland communities. He has also worked on endangered marsupial mammal populations and predation by exotic carnivores in Australia and similar systems in New Zealand
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".