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Evolution of a Perfect Simple Sequence Repeat Locus in the Context of Its Flanking Sequence

2002· article· en· W2141365767 on OpenAlexaboutno aff
Scott Blankenship, Bernie May, Dennis Hedgecock

Bibliographic record

VenueMolecular Biology and Evolution · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
FundersU.S. Fish and Wildlife ServiceMassachusetts Department of Fish and GameDepartment of Water ResourcesCalifornia Department of Fish and Game
KeywordsBiologyMicrosatelliteHaplotypeLocus (genetics)GeneticsContext (archaeology)AlleleEvolutionary biologyPopulation geneticsPopulationGenePaleontology

Abstract

fetched live from OpenAlex

Microsatellites, which have rapidly become the preferred markers in population genetics, reliably assign individual chinook salmon to the winter, fall, late-fall, or spring chinook runs in the Sacramento River in California's Central Valley (Banks et al. 2000. Can. J. Fish. Aquat. Sci. 57:915-927). A substantial proportion of this discriminatory power comes from Ots-2, a simple CA repeat, which is expected to evolve rapidly under the stepwise mutation model. We have sequenced a 300-bp region around this locus and typed 668 microsatellite-flanking sequence haplotypes to explore further the basis of this microsatellite divergence. Three sites of nucleotide polymorphism in the Ots-2 flanking sequence define five haplotypes that are shared by the Californian and Canadian populations. The Ots-2 microsatellite alleles are nonrandomly distributed among these five haplotypes in a pattern of gametic disequilibrium that is also shared among populations. Divergence between the winter run and other Central Valley stocks appears to be caused by a combination of surprisingly static evolution at Ots-2 within a context of more rapidly changing haplotype frequencies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.383

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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 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

Citations31
Published2002
Admission routes1
Has abstractyes

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