MétaCan
Menu
← Back to cohort
Record W2025478374 · doi:10.1139/z06-051

Correlated cycles of snowshoe hares and Dall’s sheep lambs

2006· article· en· W2025478374 on OpenAlexafffundvenueabout
John Wilmshurst, Roy E. Greer, Jason Henry

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsParks Canada
FundersParks Canada
KeywordsSnowshoe hareBiologyOvisPredationPopulation densityPopulationPopulation cycleEcologyBorealBovidaeAnimal scienceZoologyDemography

Abstract

fetched live from OpenAlex

We tested the hypothesis that the number of surviving lambs counted in mid-summer from a Dall’s sheep ( Ovis dalli Nelson, 1884) population on Sheep Mountain, Yukon, Canada, is correlated to the density of snowshoe hares ( Lepus americanus Erxleben, 1777) in the surrounding boreal forest. We examined correlations between the number of lambs and the number of snowshoe hares at different phases in the 10-year snowshoe hare cycle. There were significant cross-correlations between the ratio of lambs to nursery sheep and hare densities with 1- and 2-year time lags. Lamb numbers also showed clockwise rotation with respect to hare densities when points were joined chronologically. Simple population models suggest several relationships: when hare densities are high, lamb population growth rates are inversely related to hare densities; during the low phase of the hare population cycle, lamb population growth rates show density-independent fluctuations. In the absence of compelling evidence for direct interactions between Dall’s sheep and hares, we hypothesize that the inverse relationship between lamb population growth and hare density is mediated indirectly by shared predators.

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)0.002
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 score0.941
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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

Citations9
Published2006
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Zoology→Same topicWildlife Ecology and Conservation→French-language works237,207→