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Record W4399708735 · doi:10.1111/eth.13488

Burying in lake sediments: A potential tactic used by female northern map turtles to avoid male harassment

2024· article· en· W4399708735 on OpenAlexafffundabout
Grégory Bulté, Jessica A. Robichaud, Steven J. Cooke, Heath A. MacMillan, Gabriel Blouin‐Demers

Bibliographic record

VenueEthology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of OttawaCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of OttawaCarleton University
KeywordsMatingSexual conflictHarassmentOverwinteringSeasonal breederBiologyEcologyAntagonistic CoevolutionZoologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract How often males and females need to mate to maximize their fitness is a source of sexual conflict in animals. Sexual conflict over mating frequency can lead to antagonistic coevolution in which males employ tactics to coerce females into mating, while females resist or evade mating attempts by males. Here, we report on a novel burying behavior observed in female northern map turtles ( Graptemys geographica ) in Opinicon Lake, Ontario, Canada that appears to function as a tactic to avoid male detection during the mating season. Underwater videos indicated that females are heavily solicited during the mating season with over half the females being actively pursued by males. Biologgers indicated that females are less active and remain deeper than males during the mating season. Our data strongly suggest that female northern map turtles avoid intense solicitation and potential harassment by males by burying themselves in lake sediments. This behavior appears to be a low‐cost solution for females to reduce the costs of resistance and mating while they are constrained to habitats with high male densities for overwintering.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score1.000

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)0.0010.000

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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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 designBench or experimental
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

Citations1
Published2024
Admission routes3
Has abstractyes

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