MOOSE AND DEER POPULATION TRENDS IN NORTHWESTERN ONTARIO: A CASE HISTORY
Bibliographic record
Abstract
Many interrelated factors contribute to the rise and fall of white-tailed deer (Odocoileus virginianus) and moose (Alces alces) populations in the mixed boreal forests of eastern North America where these species often cohabit. A question not satisfactorily answered is why do moose populations periodically decline in a pronounced and prolonged way while deer populations continue to do well during times when habitat conditions appear good for both? Long-term historical data from the Kenora District of northwestern Ontario, Canada provided an opportunity to better understand temporal relationships between trends in deer and moose numbers and landscape-level habitat disturbances, ensuing forest succession, climate, predators, and disease. Over the past 100 years, moose and deer have fluctuated through 2 high-low population cycles. Deer numbers were high and moose numbers were low in the 1940s and 50s following a spruce budworm (Choristoneura fumiferana) outbreak. By the early 1960s, deer trended downwards and remained low during an extended period with frequent deep-snow winters; as deer declined, moose recovery was evident. Moose increased through the 1980s and 1990s as did deer, apparently in response to considerable habitat disturbance, including another spruce budworm outbreak and easier winters. However, despite conditions that were favourable for both species, moose declined markedly beginning in the late 1990s, and by 2012 were at very low levels district-wide while deer numbers remained high. Despite the moose decline being coincident with a short-lived winter tick (Dermacentor albipictus) epizootic in the early 2000s and increasing numbers of wolves (Canis lupus), we argue that the meningeal worm (Parelaphostrongylus tenuis) likely played a major role in this moose 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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".