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Record W2118491710 · doi:10.1093/jmammal/gyv062

Cyclic dynamics of a boreal southern red-backed vole population in northwestern Quebec

2015· article· en· W2118491710 on OpenAlexaffabout
Dominique Fauteux, Marianne Cheveau, Louis Imbeau, Pierre Drapeau

Bibliographic record

VenueJournal of Mammalogy · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsMinistère des Ressources naturelles et des ForêtsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à MontréalNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsBorealGeographyVolePopulationTaigaPhysical geographyEcologyForestryBiologyDemographyArchaeology

Abstract

fetched live from OpenAlex

In Fennoscandia, red-backed vole populations (Myodes spp.) often show regular fluctuations of abundance of 3- to 5-year periods. In contrast, only a few populations show evidence of cyclic fluctuations in North America. From 2001 to 2009, we livetrapped southern red-backed voles (Myodes gapperi) in 3 mature jack pine and 3 mature black spruce forest stands in the Muskuchii hills region, Quebec, Canada. We found that their density fluctuated (up to 41-fold) with a cyclical pattern and 4-year periods. Our study is the first to demonstrate cyclic dynamics in a southern red-backed vole population found in the boreal forest of North America. Regular pulse of food or heavy predation may be responsible for the fluctuations of southern red-backed voles. Furthermore, vole cycles may help elucidate the factors driving the irruptions of owls in the boreal forest.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations21
Published2015
Admission routes2
Has abstractyes

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