Distribution and diversity of squirrels on university and college campuses of the United States and Canada
Bibliographic record
Abstract
Abstract Squirrels on North American college and university campuses have been the subject of much animated conversation in informal venues, but a systematic assessment of their distribution across this habitat type has not been undertaken until now. We collected reports of squirrel species’ presence and absence from faculty experts at 536 campuses in Canada and the continental United States, and found that squirrels are nearly ubiquitous on campuses (95% had at least one species, and 40% had three or more), but that only a select few species are common campus residents. Foremost among these is the eastern gray squirrel (Sciurus carolinensis—on 62% of surveyed campuses), followed by the eastern fox squirrel (Sciurus niger), eastern chipmunk (Tamias striatus), North American red squirrel (Tamiasciurus hudsonicus), and woodchuck (Marmota monax). Eastern gray and fox squirrels partition campuses with one another, both in their shared native/core range and on the West Coast. While these invasive eastern species are present on campuses on the West Coast, notably, contingency analysis did not provide evidence that they are the primary factor discouraging western gray squirrels (Sciurus griseus) from using these habitats. The inventory and analyses presented here can provide a basis for longitudinal studies both within and across campuses and may be productively combined with initiatives that involve students in research.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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".