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Record W2024932467 · doi:10.1098/rsbl.2014.0336

Emerging issues in the evolution of animal nuptial gifts

2014· article· en· W2024932467 on OpenAlexaff
Sara M. Lewis, Karim Vahed, Joris M. Koene, Leif Engqvist, Luc F. Bussière, Jennifer C. Perry, Darryl Gwynne, Gerlind U. C. Lehmann

Bibliographic record

VenueBiology Letters · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologySexual selectionIntersection (aeronautics)Selection (genetic algorithm)Evolutionary biologyField (mathematics)EcologyConceptual frameworkBiological evolutionEpistemologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Uniquely positioned at the intersection of sexual selection, nutritional ecology and life-history theory, nuptial gifts are widespread and diverse. Despite extensive empirical study, we still have only a rudimentary understanding of gift evolution because we lack a unified conceptual framework for considering these traits. In this opinion piece, we tackle several issues that we believe have substantively hindered progress in this area. Here, we: (i) present a comprehensive definition and classification scheme for nuptial gifts (including those transferred by simultaneous hermaphrodites), (ii) outline evolutionary predictions for different gift types, and (iii) highlight some research directions to help facilitate progress in this field.

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)0.010
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.017
Scholarly communication0.0050.009
Open science0.0020.003
Research integrity0.0030.005
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 head0.016
GPT teacher head0.252
Teacher spread0.236 · 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

Citations93
Published2014
Admission routes1
Has abstractyes

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