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Record W4417491991 · doi:10.1111/ibi.70022

Are morphometric traits cryptic indicators of sexual size dimorphism in ‘monomorphic’ species? Evidence from the King Vulture ( <i>Sarcoramphus papa</i> )

2025· article· en· W4417491991 on OpenAlexfundno aff
Enzo Basso, Jonathan Vergara‐Amado, Sarah Wicks, Eleanor Flatt, Diego Rolim Chulla, Flor M. Pérez Mullisaca, Caleb Jonatan Quispe Quispe, R. Delgado, Andrew Whitworth, Claudio Verdugo, Christopher Beirne

Bibliographic record

VenueIbis · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersBeijing Innovation Center for Future ChipUniversidad Austral de ChileInternational Conservation Fund of CanadaGordon and Betty Moore Foundation
KeywordsSexual dimorphismMonomorphismSexingVulturePopulationMorphometrics

Abstract

fetched live from OpenAlex

Understanding sexual dimorphism is essential for ecological and evolutionary research, as it can influence species adaptation, population dynamics and the development of effective conservation strategies. Evidence suggests that sexual size dimorphism is absent or subtle in over 50% of bird species – including most New World vultures (Cathartiformes). We employed a molecular sexing and morphometric analysis approach to assess whether this pattern is present in the King Vulture Sarcoramphus papa and to determine whether males and females can be distinguished morphometrically. We present evidence of moderate male‐biased sexual size dimorphism in the King Vulture, showing that head and bill measurements – and to a lesser extent, wing traits – can be used to discriminate between sexes based on extreme values, thus challenging the traditional view of monomorphism in cathartids and highlighting the value of morphometric analyses in detecting cryptic sexual differences in species considered monomorphic.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.244
Teacher spread0.207 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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