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Record W1985213717 · doi:10.1139/z09-017

Large-scale spatiotemporal variation in calf sex ratio in moose (<i>Alces alces</i>): an effect of density-dependent decrease in maternal condition?

2009· article· en· W1985213717 on OpenAlexvenueno aff
Kari Bjørneraas, Erling J. Solberg, Ivar Herfindal, Bernt‐Erik Sæther

Bibliographic record

VenueCanadian Journal of Zoology · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyAnimal scienceSex ratioPopulationPopulation densityDensity dependenceDemography

Abstract

fetched live from OpenAlex

The harvest of Norwegian moose ( Alces alces (L., 1758)) is directed towards certain sex and age classes to maximize yield in terms of meat or number of animals. Observed side effects are declining numbers of calves per female and proportions of adult males, which may affect other demographic variables. Using long-term data, we examined whether spatiotemporal variation in the calf sex ratio was related to changes in (i) density of harvested moose, (ii) recruitment rate, and (or) (iii) the composition of the adult segment of the population. We found declining proportions of male calves in the autumn harvest over time associated with decreasing recruitment rates. Similarly, the proportion of male calves was lower when density of harvested moose was high. We suggest that the decrease in proportion of male calves was caused by increased prenatal or postnatal mortality rates of males, possibly owing to a density-dependent decline in maternal body condition. Proportion of male calves increased with the proportion of adult males in the population the previous year, indicating that low proportions of adult males may lead to lower male recruitment, particularly at high densities. Further declines in proportions of male calves recruited may be avoided by reducing the population density and changing the demographic composition of the harvest.

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)0.002
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 score0.830
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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