Positive relationship between non-native and native squirrels in an urban landscape
Bibliographic record
Abstract
Paradoxically, non-native species sometimes displace native species that appear to be well adapted to local landscapes. That many landscapes have been altered by humans, creating habitat suitable for non-native species, helps explain this apparent paradox. We asked whether the abundance of native Douglas ( Tamiasciurus douglasii (Bachman, 1839)) and northern flying ( Glaucomys sabrinus (Shaw, 1801)) squirrels was best explained by the abundance of non-native eastern grey squirrels ( Sciurus carolinensis Gmelin, 1788), the proportion of urban development, or both using available squirrel abundance data from wildlife shelters and land-use maps. There was no evidence that non-native squirrels replaced native squirrels given that their abundances were positively related, whereas native squirrels varied negatively with the amount of development. The best model explaining variation in the abundance of Douglas and northern flying squirrels incorporated both eastern grey squirrels and development, which is consistent with the hypothesis that regional declines in native squirrels are more likely to be predicated by the alteration of native conifer habitats by humans independent of the effects of non-native squirrels.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".