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Record W2020864020 · doi:10.1139/z05-130

Courtship displays and mounting calls are honest, condition-dependent signals that influence mounting success in Hermann's tortoises

2005· article· en· W2020864020 on OpenAlexvenueno aff
Paolo Galeotti, Roberto Sacchi, Mauro Fasola, Daniele Pellitteri‐Rosa, Manuela Marchesi, Donato Ballasina

Bibliographic record

VenueCanadian Journal of Zoology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersMinistero dell'Università e della Ricerca
KeywordsCourtshipTortoiseBiologyCourtship displayZoologySexual selectionRange (aeronautics)Ecology

Abstract

fetched live from OpenAlex

Like other terrestrial tortoises, the courtship behaviour of Hermann's tortoises (Testudo hermanni Gmelin, 1789) is based on a multiple signalling system that involves visual, olfactory, tactile, and acoustic signals. In this study, we analysed relationships between male morphology, hematological profile, courtship intensity, vocalizations, and mounting success in Hermann's tortoises breeding in semi-natural enclosures to investigate the effects of male condition on signals exhibited during courtship and on their mounting success. Results showed that mounting success of Hermann's tortoise males was positively affected by the number of sexual interactions/h, number of bites given to the female during interactions, and by call rate and frequency-modulation range. Call rate, frequency-modulation range, and number of sexual interaction/h increased with hematocrit value, while number of bites given to females decreased with leukocyte concentration. In conclusion, courtship signals exhibited by Hermann's tortoise males, including vocalizations, reliably reveal different components of male condition, and females may use these multiple traits to choose high-quality partners. This is the first study documenting the condition-dependent nature of tortoise courting signals and their effect on male mounting success.

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)0.000
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 score0.209
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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

Citations43
Published2005
Admission routes1
Has abstractyes

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