Habitat-specific distribution and abundance of arctic ground squirrels (<i>Urocitellus parryii plesius</i>) in southwest Yukon
Bibliographic record
Abstract
Arctic ground squirrels ( Urocitellus parryii plesius (Osgood, 1900); formerly Spermophilus parryii plesius Osgood, 1900) were studied in three distinct habitat types (boreal forest, low-elevation meadows, and alpine meadows) in the Kluane region of the southwest Yukon Territory, Canada, from 2008 to 2010 to determine if populations in these different habitats provide evidence for habitat-specific distribution and abundance. Abundance in the boreal forest has been shown to be synchronous with the cycle of snowshoe hares ( Lepus americanus Erxleben, 1777) in the region owing to shared predators. We predicted that populations in the boreal forest would be low because of the current low phase in the cycle of snowshoe hares, and that in low-altitude meadows and alpine meadows, ground squirrels would be relatively abundant. Late-summer densities differed significantly between habitat types with 0.38 ± 0.13 squirrel/ha (mean ± 1 SE) in boreal-forest habitat, 1.25 ± 0.22 squirrel/ha in low-altitude-meadow habitat, and 5.7 ± 0.22 squirrels/ha in alpine-meadow habitat. In 2009, populations were extirpated from boreal-forest habitat, while densities in low-elevation meadows and alpine meadows were 1.6 ± 0.34 squirrel/ha and 6.1 ± 0.7 squirrels/ha, respectively. The current absence of squirrels from the boreal forest and the persistence of populations in low-elevation-meadow and alpine-meadow habitat suggest that source–sink dynamics may exist between boreal-forest and meadow habitat types.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".