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Record W2624609520 · doi:10.1071/zo16051

Limited sex bias in the fine-scale spatial genetic structure of the eastern grey kangaroo and its relationship to habitat

2017· article· en· W2624609520 on OpenAlexaff
Linda E. Neaves, Michael W. Roberts, Catherine A. Herbert, Mark D. B. Eldridge

Bibliographic record

VenueAustralian Journal of Zoology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsCenovus Energy (Canada)
Fundersnot available
KeywordsBiological dispersalPhilopatryBiologyGenetic structureEcologyRange (aeronautics)PopulationSpatial ecologyZoologyDemographyGenetic variationGenetics

Abstract

fetched live from OpenAlex

Animals exhibit a range of dispersal strategies that impact on the organisation of individuals and can be influenced by both the environment and population demography. We examined the fine-scale spatial genetic structure and patterns of relatedness in 139 adult eastern grey kangaroos (Macropus giganteus) to test predictions of male-biased dispersal and female philopatry in comparison with previous studies in different environments in the species’ distributions. We found evidence of limited differences between the sexes, with little spatial genetic structure in both males and females. The levels of relatedness among females in close proximity were not indicative of close relatives (e.g. mother–daughter) and there was no evidence of matrilineal structure. Among males, there was little evidence of genetic structure. Although our results are, in general, consistent with those of previous studies, we found study-specific differences in the extent of genetic structure that appear to be related to differences in environmental and demographic conditions across the distribution. This highlights the need for additional research focussing on populations from a range of environmental conditions.

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 score0.014
Threshold uncertainty score0.413

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 head0.045
GPT teacher head0.264
Teacher spread0.219 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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