The Role of Red Squirrels and Arctic Ground Squirrels
Bibliographic record
Abstract
Abstract A consistent feature throughout the boreal forest of North America is the rattle call of the red squirrel, Tamiasciurus hudsonicus, as it advertises its whereabouts to con specifics. Like the snowshoe hare, the red squirrel’s distribution encompasses the entire boreal forest (see figure 2.7). The “keek keek” call of a second squirrel species, the arctic ground squirrel (Spermophilus parryii, the siksik of the Inuit), is also heard in the boreal forests of northwestern North America (Banfield 1974). In terms of biomass of herbivores in these forests, these two squirrels are the second and third most important, respectively, after snowshoe hares (see figure 1.2). Both squirrel species could serve as alternate food sources for the many predators who eat primarily snowshoe hares. However, before our study, no one had investigated experimentally the possible linkages between populations of these squirrels and the snowshoe hare population cycle. The conventional wisdom is that any link would be a secondary one, as predators switch from hares to squirrels during the hare decline. Though both squirrels are active during the summer and thus potentially available to predators, only red squirrels remain active during the long boreal winter and are one of the few alternate prey available to hare predators.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".