MétaCan
Menu
Back to cohort

Muskoxen in the high Arctic‐temporal and spatial differences in body size

2007· article· en· W2146526961 on OpenAlexaffabout
B. Lyberth, Arild Landa, John A. Nagy, Anne Loison, Carsten Riis Olesen, Anne Gunn, Mads C. Forchhammer

Bibliographic record

VenueJournal of Zoology · 2007
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsInstitute for Circumpolar Health Research
Fundersnot available
KeywordsArcticBiologyReproductionGroenlandiaPopulationPopulation densityEcologyDemographyZoology

Abstract

fetched live from OpenAlex

Abstract The life history of ungulates is affected by factors such as climate, population density and resource availability. With focus on the muskoxen Ovibos moschatus living in Kangerlussuaq in western Greenland, Jameson Land in north‐eastern Greenland and on Banks and Victoria Islands in northern Canada, we tested spatial variation in life‐history traits measured by mandibular growth. In accordance with expectations, we found that muskoxen in the southernmost and low Arctic area (Kangerlussuaq) grew faster, matured earlier, reproduced earlier, reached larger adult size and additionally had a higher reproduction than muskoxen living in the more northern areas. In the Kangerlussuaq population, mandible lengths in adult males changed temporally with density, with significant smaller adult males present in high population densities in western Greenland. It was especially the male mandible lengths that responded to environmental factors. In females, spatial differences were less pronounced than in males and is probably explained by females facing a trade‐off between investment in own growth and reproduction, whereas a large body size is more important for the males, which are exposed to sexual selection. This explanation was, furthermore, supported by the fact that the calf percentage was higher in western Greenland than in any of the other studied areas in spite of the density‐dependent effects detected within the male gender.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.335
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.001
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 head0.032
GPT teacher head0.354
Teacher spread0.322 · 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

Citations7
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueJournal of ZoologySame topicIndigenous Studies and EcologyFrench-language works237,207