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Record W2123683558 · doi:10.1139/z08-107

Intersexual niche divergence in northern map turtles (Graptemys geographica): the roles of diet and habitat

2008· article· en· W2123683558 on OpenAlexaffvenueabout
Grégory Bulté, Marie‐Ange Gravel, Gabriel Blouin‐Demers

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBiologySexual dimorphismHabitatEcologyPredationPopulationCompetition (biology)NicheZoologyDemography

Abstract

fetched live from OpenAlex

Sexual dimorphism in body size and in trophic morphology are common in animals and are often concordant with patterns of habitat use and diet. Proximate factors leading to intersexual differences in habitat use, however, are challenging to unravel because these differences may stem from sexual dimorphism or may be caused by intersexual competition. Intersexual differences in diet and habitat use are common in size dimorphic reptiles. In this study, we investigated factors contributing to intersexual differences in diet and habitat use in a population of northern map turtles ( Graptemys geographica (Le Sueur, 1817)) from Ontario, Canada. Using radiotelemetry, we showed that in a lake map turtles do not exhibit intersexual differences in habitat use, in contrast to river populations. Patterns of habitat use were also inconsistent with prey distribution. The lack of intersexual habitat use differences in our lake population, despite marked differences in prey distribution, also indicated that intersexual habitat use differences documented in river populations are a consequence of sexual dimorphism in swimming capacity. Using stable isotope analysis and fecal analysis, we found a large dietary overlap between males and females, indicating no intersexual competition for food. Patterns of prey selection in females, however, were concordant with the reproductive role hypothesis.

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 score0.771
Threshold uncertainty score0.548

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 studies0.0000.001
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 head0.010
GPT teacher head0.185
Teacher spread0.175 · 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

Citations48
Published2008
Admission routes3
Has abstractyes

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