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TEMPORAL VARIATION AT THE MHC CLASS IIB IN WILD POPULATIONS OF THE GUPPY (<i>POECILIA RETICULATA</i>)

2010· article· en· W2082770902 on OpenAlexaff
Bonnie A. Fraser, Indar W. Ramnarine, Bryan D. Neff

Bibliographic record

VenueEvolution · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsWestern University
Fundersnot available
KeywordsPoeciliaBiologyGuppyVariation (astronomy)Evolutionary biologyZoologyMHC class IMajor histocompatibility complexGeneticsFish <Actinopterygii>GeneFishery

Abstract

fetched live from OpenAlex

Understanding genetic diversity in natural populations is a fundamental objective of evolutionary biology. The immune genes of the major histocompatibility complex (MHC) are excellent candidates to study such diversity because they are highly polymorphic in populations. Although balancing selection may be responsible for maintaining diversity at these functionally important loci, temporal variation in selection pressure has rarely been examined. We examine temporal variation in MHC class IIB diversity in nine guppy (Poecilia reticulata) populations over two years. We found that five of the populations changed significantly more at the MHC than at neutral (microsatellite) loci as measured by F(ST), which suggests that the change at the MHC was due to selection and not neutral processes. Additionally, pairwise population differentiation measures at the MHC were higher in 2007 than in 2006, with the signature of selection changing from homogenizing to diversifying selection or neutral evolution. Interestingly, within the populations the magnitude of the change at the MHC between years was related to the change in the proportion of individuals infected by a common parasite, indicating a link between genetic structure and the parasite. Our data thereby implicate temporal variation in selective pressure as an important mechanism maintaining diversity at the MHC in wild populations.

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 score0.390
Threshold uncertainty score0.983

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations39
Published2010
Admission routes1
Has abstractyes

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