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Record W3197529618 · doi:10.1007/s10682-021-10128-7

Reproductive effort affects subsequent horn growth in sexually dimorphic male ungulates

2021· article· en· W3197529618 on OpenAlexaff
Christian S. Willisch, Peter Neuhaus

Bibliographic record

VenueEvolutionary Ecology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Calgary
FundersBerner Fachhochschule
KeywordsAnimal ecologyPolygynySexual dimorphismBiologyFrench hornEcologySexual selectionReproductionReproductive successBehavioral ecologyZoologyMammalDemographyPopulationPsychology

Abstract

fetched live from OpenAlex

Abstract Trade-offs between reproductive effort and subsequent growth in males are not well explored, despite their relevance in questions of individual energy allocation. Regarding the growth of sexual secondary characters in polygynous breeding male mammals, indeed, no conclusive studies exist. We investigated in male Alpine ibex (Capra ibex) the relationship between their behavioral reproductive effort, current horn size, and subsequent horn growth. While controlling for age, no evidence was found for male behavioral reproductive effort during the rut being affected by their horn size. On the other hand, reproductive effort significantly decreased age-specific horn growth during the following summer. Our study provides evidence that growth of secondary sexual characters is traded against behavioral investments in reproduction in a male mammal. It bears important implications for the understanding of energy allocation between various life-history components and the evolutionary ecology of secondary sexual characters.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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