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Record W4389616330 · doi:10.1111/rec.14063

Small mammal responses to biosolids on grazed rangelands in British Columbia

2023· article· en· W4389616330 on OpenAlexaffabout
Jennifer K. Meineke, Francis I. Doyle, Louisa Oukil, Karen E. Hodges

Bibliographic record

VenueRestoration Ecology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsRangelandBiosolidsHabitatEcologyWildlifePlant communityNative plantPeromyscusEnvironmental scienceGrazingGeographyBiologyAgronomyIntroduced speciesEcological succession

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.022
GPT teacher head0.237
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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