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Record W91883366 · doi:10.2981/wlb.2000.035

The influences of density on growth and reproduction in moose <i>Alces alces</i>

2000· article· en· W91883366 on OpenAlexafffundabout
Steven H. Ferguson, Alan R. Bisset, François Messier

Bibliographic record

VenueWildlife Biology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMinistry of Natural Resources and ForestryLakehead UniversityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPredationEcologyPopulation densityReproductionBiologyProductivityPopulationDensity dependenceRange (aeronautics)Population sizeDemography

Abstract

fetched live from OpenAlex

We test whether high moose density results in smaller moose, slower growth rates, lower reproductive rates, and more variable year‐to‐year population size by comparing demographic characteristics of 15 Canadian moose Alces alces populations that spanned a range of population density (0.08–4.5 moose/ km 2 ). Density negatively affected growth rate, reproductive rates and recruitment. We argue that primary productivity, measured as percent forest cover, and natural predation link density to reproduction in moose. Populations that lived in greater forest cover and experienced greater natural predation were associated with more predictable year‐to‐year variation in population size. In contrast, moose populations living in areas of low forest cover and low natural predation experienced greater density independent food limitation and greater unpredictability in population size. Thus, moose populations living in areas of low primary productivity and low natural predation show less persistence and require greater conservation efforts.

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 score0.012
Threshold uncertainty score0.205

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations37
Published2000
Admission routes3
Has abstractyes

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