What Drives the 10-year Cycle of Snowshoe Hares?
Bibliographic record
Abstract
In 1831 the manager of a Hudson's Bay Company post in northern Ontario wrote to the head office in London. The local Ojibway Indians were starving, he reported, because of a scarcity of “rabbits,” and they were unable to trap for furs because they spent all their time fishing for food (Winterhalder 1980). These shortages of so-called rabbits, which apparently occurred approximately every 10 years, are regularly mentioned in Canadian historical documents from the 18th and 19th centuries. Those rabbits were in fact snowshoe hares (Lepus americanus), and their 10-year cycle is one of the most intriguing features of the ecology of the boreal forest. Ten-year cycles were first analyzed quantitatively when wildlife biologists began to plot the fur trading records of Hudson's Bay Company during the early 1900s. The Hudson's Bay Company, established in 1671, kept meticulous records of the numbers of furs traded from different posts spread across Canada. The most famous time series drawn together from those records was that of Canada lynx (Elton and Nicholson 1942; Figure 1). The lynx is a specialist predator of snowshoe hares, and the rise and fall in lynx numbers mirrors, with a slight time lag, the rise and fall of snowshoe hare populations across the boreal region.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".