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Record W2320351037 · doi:10.1139/z11-004

Fluctuations in lemming populations in north Yukon, Canada, 2007–2010

2011· article· en· W2320351037 on OpenAlexafffundvenueabout
Charles J. Krebs, Donald G. Reid, Alice J. Kenney, Scott F. Gilbert

Bibliographic record

VenueCanadian Journal of Zoology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsYukon UniversityWildlife Conservation Society CanadaUniversity of British Columbia
FundersParks CanadaAurora Research Institute
KeywordsMicrotusTundraPopulation cycleArcticSnowEcologyPopulationPopulation densityBiologyGeographyPredationDemography

Abstract

fetched live from OpenAlex

We estimated population density of brown lemmings ( Lemmus sibiricus (Kerr, 1792)), Greenland collared lemmings ( Dicrostonyx groenlandicus (Traill, 1823)), and tundra voles ( Microtus oeconomus (Pallas, 1776)) on Herschel Island from 2007 to 2010 by mark–recapture on three live-trapping areas. Limited data were also available from Komakuk Beach on the north Yukon coast. In contrast to most previous studies, brown and collared lemmings were partly out of phase. Brown lemmings on Herschel reached peak density in 2007–2008 and were low in 2009–2010, while collared lemmings were at peak density in 2007–2008 and again in 2010. Large adult male body size was characteristic of peak populations. Brown lemmings increased dramatically in the peak summer of 2008 and collared lemmings increased rapidly when winter breeding under the snow was successful in 2009–2010. By contrast, at Komakuk Beach, we could see no clear signs of fluctuations in these three species. Winter snow conditions may be too severe for population persistence on the coastal plain along the north coast of the Yukon. Further work is needed to unravel why peak lemming densities are so variable among sites and why lemming fluctuations are so pronounced on the arctic coastal plain of Alaska and virtually absent on the coastal plain of the north Yukon.

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 score0.058
Threshold uncertainty score0.998

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)0.0030.000

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 head0.043
GPT teacher head0.234
Teacher spread0.191 · 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

Citations43
Published2011
Admission routes4
Has abstractyes

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