Small mammal responses to biosolids on grazed rangelands in British Columbia
Bibliographic record
Abstract
Grasslands are globally declining due to habitat conversion, overgrazing, climate change, and changes in fire and drought regimes. Degraded grasslands are less able to support wildlife species, which contributes to the imperilment of many species. Biosolids are used by some ranchers as an organic amendment to support more plant growth and in turn more livestock. We worked on a large cattle ranch in central British Columbia, Canada, to determine how biosolids amendment affected small mammal populations, as mice and voles are major prey for many predators and contribute to seed dispersal and underground dynamics. We found that biosolids‐amended pastures had more grass cover and supported fewer deermice ( Peromyscus maniculatus ) than did unamended pastures. Voles were scarce, likely due to a cyclic low. Although we sampled sites that had had biosolids applied 1 or 3 years prior to our work started, deermouse populations were similarly low across these sites. The reduction in deermice on sites with biosolids relative to unamended sites could be a signal of habitat restoration, because deermice prefer disturbed habitats rather than ones with high plant cover. Grass cover was more than twice as high on sites amended with biosolids, making these sites less suitable for deermice.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".