Bibliographic record
Abstract
Two population oscillations dominate terrestrial community dynamics in northern Canada. In the boreal forest, the snowshoe hare (Lepus americanus) fluctuates in cycles with an 8-10 year periodicity and in tundra regions lemmings typically fluctuate in cycles with a 3-4 year periodicity. I review 60 years of research that has uncovered many of the causes of these population cycles, outline areas of controversy that remain and suggest key questions to address. Lemmings are keystone herbivores in tundra ecosystems because they are a key food resource for many avian and mammalian predators and are a major consumer of plant production. There remains much controversy over the role of predation, food shortage and social interactions in causing lemming cycles. Predation is well documented as a significant mortality factor limiting numbers. Food shortage is less likely to be a major limiting factor on population growth in lemmings. Social interactions might play a critical role in reducing the rate of population growth as lemming density rises. Snowshoe hares across the boreal forest are a key food for many predators and their cycles have been the subject of large-scale field experiments that have pinpointed predation as the key limiting factor causing these fluctuations. Predators kill hares directly and indirectly stress them by unsuccessful pursuits. Stress reduces the reproductive rate of female hares and is transmitted to their offspring who also suffer reduced reproductive rates. The maternal effects produced by predation risk induce a time lag in the response of hare reproductive rate to density, aiding the cyclic dynamics.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".