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Record W2012141021 · doi:10.1139/g08-060

Y-specific sequences and polymorphisms in rainbow trout and Chinook salmon

2008· article· en· W2012141021 on OpenAlexvenueno aff
Joseph P. Brunelli, Kelsey Wertzler, Kyle Sundin, Gary H. Thorgaard

Bibliographic record

VenueGenome · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsnot available
FundersNational Institute of Environmental Health Sciences
KeywordsRainbow troutBiologyChinook windSexingGeneticsOncorhynchusY chromosomeLocus (genetics)ChromosomeSex linkageGenetic markerEvolutionary biologyFisheryGeneFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Improved methods for genetically sexing salmonids and for characterization of Y-chromosome homologies between species can contribute to understanding the evolution of sex chromosomes and sex-determining mechanisms. In this study we have explored 12.5 kb of Y-chromosome-specific sequence flanking the previously described OtY2 locus in Chinook salmon (Oncorhynchus tshawytscha) and 21 kb of homologous rainbow trout (Oncorhynchus mykiss) Y-chromosome-specific sequence. This is the first confirmed Y-specific sequence for rainbow trout. New Y-specific markers are described for Chinook salmon (OtY3) and rainbow trout (OmyY1), which are readily detected by PCR assays and are advantageous because they also produce autosomal control amplification products. Additionally, AFLP analysis of Chinook salmon yielded another potential Y-chromosome marker. These descriptions will facilitate genotypic sexing and should be useful for population studies of Y-chromosome polymorphisms and for future studies to characterize what appears to be a common sex-determining mechanism between these species.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations128
Published2008
Admission routes1
Has abstractyes

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