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Record W2013335047 · doi:10.1139/z08-019

Influence of rodent abundance on nesting success of prairie waterfowl

2008· article· en· W2013335047 on OpenAlexafffundvenueabout
Rodney W. Brook, Maria Pasitschniak-Arts, David W. Howerter, François Messier

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of SaskatchewanMinistry of Natural Resources and ForestryDucks Unlimited Canada
FundersU.S. Fish and Wildlife ServiceInstitute for Wetland and Waterfowl Research, Ducks Unlimited CanadaNational Fish and Wildlife Foundation
KeywordsWaterfowlPredationVoleBiologyAbundance (ecology)EcologyNest (protein structural motif)MicrotusPredatorPiscivoreAnasHabitatNesting (process)Population

Abstract

fetched live from OpenAlex

Most waterfowl nesting failure in the prairie biome is attributed to predation. However, the contribution of small mammal abundance to the prairie predator–prey cycle and how this affects waterfowl productivity is not known. We modelled seasonal variability of nesting success, including a number of habitat and nest-related variables, to quantify influence of rodent abundance for prairie nesting waterfowl for six study sites in the Prairie Pothole Region of Canada, 1996–1998. We estimated there is a curvilinear relationship between the abundance of meadow voles ( Microtus pennsylvanicus (Ord, 1815)) and the nesting success of ducks. The relationship has characteristics of the alternate prey hypothesis at low vole density and characteristics of the shared prey hypothesis at higher densities. At low vole densities, duck nests appear to be buffered from predation by voles but, at higher densities, nesting success was affected negatively. We recommend that predator–prey dynamics should be included as an integral part of management plans for nesting waterfowl and suggest further research using rigorous experiment design to elucidate mechanisms and pathways responsible for this observed relationship.

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 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 score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.232
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; a candidate call from one teacher head, 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

Citations18
Published2008
Admission routes4
Has abstractyes

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