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Record W2006120011 · doi:10.1139/g09-049

Fine mapping of the epistatic suppressor gene (<i>esp</i>) of a recessive genic male sterility in rapeseed (<i>Brassica napus</i> L.)

2009· article· en· W2006120011 on OpenAlexvenueno aff
Zhenghua Xu, Yanzhou Xie, Dengfeng Hong, Pingwu Liu, Guangsheng Yang

Bibliographic record

VenueGenome · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Reproductive Biology
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyGeneticsSterilityRapeseedGeneGene mappingGenetic markerEpistasisAmplified fragment length polymorphismBrassicaPopulationPositional cloningCytoplasmic male sterilityMarker-assisted selectionMutantChromosomeBotany

Abstract

fetched live from OpenAlex

9012AB, a recessive genic male sterility (RGMS) line derived from spontaneous mutation in Brassica napus, has been playing an important role in rapeseed hybrid production in China. The male sterility of 9012AB is controlled by two recessive genes (ms3 and ms4) interacting with one recessive epistatic suppressor gene (esp). The objective of this study was to develop PCR-based markers tightly linked to the esp gene and construct a high-resolution map surrounding the esp gene. From the survey of 512 AFLP primer combinations, 3 tightly linked AFLP markers were obtained and successfully converted to codominant or dominant SCAR markers. Furthermore, a codominant SSR marker (Ra2G08) associated with the esp gene was identified through genetic map integration. For fine mapping of the esp gene, these PCR-based markers were analyzed in a large BC1 population of 2545 plants. The esp gene was then genetically restricted to a region of 1.03 cM, 0.35 cM from SSR marker Ra2G08 and 0.68 cM from SCAR marker WSC6. The SCAR marker WSC5 co-segregated with the target gene. These results lay a solid foundation for map-based cloning of esp and will facilitate the selection of RGMS lines and their temporary maintainers.

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 score0.315
Threshold uncertainty score0.479

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 head0.010
GPT teacher head0.224
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 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

Citations12
Published2009
Admission routes1
Has abstractyes

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