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Record W2147558932 · doi:10.1644/13-mamm-a-022.1

Landscape-scale features affecting small mammal assemblages on the northern Great Plains of North America

2013· article· en· W2147558932 on OpenAlexafffund
Leanne M. Heisler, Christopher M. Somers, Troy I. Wellicome, Ray G. Poulin

Bibliographic record

VenueJournal of Mammalogy · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsRoyal Saskatchewan MuseumUniversity of AlbertaUniversity of Regina
FundersMitacs
KeywordsMicrotusSorexPeromyscusEcologyGrasslandMammalBiologySigmodon hispidusApodemusShrewGeography

Abstract

fetched live from OpenAlex

Abstract Little is known about the macrohabitat associations of rodents and shrews in prairie landscapes because of the logistic constraints of conventional trapping. We used the remains of 60,972 small mammals in owl pellets to assess factors affecting small mammal composition across 4.3 million hectares of the northern Great Plains of North America. Cropland with clay soils was dominated by deer mice (Peromyscus maniculatus), whereas areas with higher proportions of native grassland and moderately sandy soils supported communities with more sagebrush voles (Lemmiscus curtatus). Areas with clay soils and higher annual precipitation were associated with higher proportions of house mice (Mus musculus), meadow voles (Microtus pennsylvanicus), and shrews (Blarina brevicauda and Sorex species), whereas drier areas with sandier soils and lower annual precipitation were dominated by olive-backed pocket mice (Perognathus fasciatus) and northern grasshopper mice (Onychomys leucogaster). Contrary to extrapolations of previous smaller-scale efforts, soil texture was the primary landscape feature driving small mammal composition in our study, whereas agricultural cropland significantly altered the composition of these assemblages. These associations demonstrate the importance of considering macrohabitats encompassing entire populations.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.195
Teacher spread0.186 · 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; a candidate call from one teacher head, not a consensus.

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

Citations23
Published2013
Admission routes2
Has abstractyes

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