Multiple factors limit use of local sites by Elliot’s short-tailed shrews (<i>Blarina hylophaga</i>) in tallgrass prairie
Bibliographic record
Abstract
Spatial variation in abundance has been attributed to habitat heterogeneity and patchiness. Our goal in this research was to understand what factors were associated with spatial patterns of habitat use by Elliot’s short-tailed shrews ( Blarina hylophaga Elliot, 1899) in tallgrass prairie. Our modeling efforts were based on 20 years (1981–2000) of presence–absence data for shrews at each of 20 stations (local site) along 14 permanent traplines on Konza Prairie Biological Station, Kansas, USA. A logistic model accurately predicted the presence of short-tailed shrews at a local site. Probability of shrew occurrence decreased as amount of precipitation decreased, slope steepness increased, grazing increased, or burned area within 500 m of a local site increased. However, when amount of precipitation was low, area burned was high, or grazing occurred, shrews were uncommon and responded little to the other variables. Numbers of shrews were negatively related only to numbers of deer mice ( Peromyscus maniculatus (Wagner, 1845)), a mouse that selects burned and grazed habitats that shrews avoid. Our observations suggest that multiple environmental factors limit use of local sites, whereas competition with other species does not. Our results can inform decisions related to conservation of biodiversity given management practices in this endangered ecosystem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".