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Record W2063040399 · doi:10.1139/z05-143

Female wolverine (<i>Gulo gulo</i>) reproduction: reproductive costs and winter food availability

2005· article· en· W2063040399 on OpenAlexvenueno aff
Jens Persson

Bibliographic record

VenueCanadian Journal of Zoology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersNaturvårdsverketKempe Foundation
KeywordsReproductionBiologyTraitReproductive successEcologyDemographyPopulation

Abstract

fetched live from OpenAlex

An individual has only a given amount of resources, and therefore an increase in one demographic trait results in a trade-off that necessitates a decrease in a different demographic trait. In general, the main factor determining an individual mammal's reproductive investment is food supply. This study addresses how female wolverine (Gulo gulo (L., 1758)) reproduction is limited. I tested two complementary hypotheses: (1) current reproduction is affected by the costs of reproduction in the preceding year and (2) current reproduction is affected by food availability in the current winter. I addressed the first hypothesis by comparing reproductive rates of females in relation to their reproductive effort in the preceding year. I experimentally tested the second hypothesis by comparing reproductive rates of food-supplemented females versus non-supplemented females. Reproduction incurred costs on female wolverines that affected future reproduction, and reproductive costs appeared to be related to the duration of parental care. Reproduction was higher for food-supplemented females than for non-supplemented females, even though all food-supplemented females had reproduced the preceding year. This study suggests that reproduction is limited by winter food availability and that additional food can compensate for reproductive costs. Thus, I suggest that female wolverine reproduction is determined by their condition in winter, which is a result of the combined effect of reproductive costs and winter food availability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.205
Teacher spread0.194 · 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.

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

Citations102
Published2005
Admission routes1
Has abstractyes

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