Habitat selection by arctic ground squirrels (<i>Spermophilus parryii</i>)
Bibliographic record
Abstract
Arctic ground squirrels (Spermophilus parryii) are abundant, colonial, noncyclical small mammals whose within-range distribution patterns have received little scientific attention. The distribution of arctic ground squirrels may drive the abundance and spatial arrangement of other arctic ecosystem components, because they serve as a prey item, plant predator, and ecological engineer. We modeled ground squirrel habitat selection using exponential resource selection functions. To determine whether the presence of adjacent squirrel burrows influenced our indices of habitat selection, we evaluated autocovariates in our models that accounted for the predicted relative probability of ground squirrel presence within neighborhoods of various sizes around each focal area. Our models demonstrated selection by ground squirrels for well-drained substrates and sloped or convex terrain, and against wet and hummocky terrain and areas of high greenness. At a large spatial scale highly selected habitat was predicted to be more limited spatially in treed areas than in tundra. Evaluation of models incorporating autocovariates demonstrated that ground squirrel distribution was not determined by the presence of other burrows. Given observed patterns of habitat selection, climate change and attendant changes to vegetation and soils could impact the ecology of arctic ground squirrels, with consequent effects on arctic plant communities and predator–prey cycles.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".