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Record W2748595563 · doi:10.1139/gen-2017-0100

Rapid and targeted introgression of <i>fgr</i> gene through marker-assisted backcrossing in rice (<i>Oryza sativa</i> L.)

2017· article· en· W2748595563 on OpenAlexvenueno aff
Acga Cheng, Ismanizan Ismail, Mohamad Osman, Habibuddin Hashim, Nur Samahah Mohd Zainual

Bibliographic record

VenueGenome · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsnot available
FundersKementerian Pertanian dan Industri Asas Tani Malaysia
KeywordsBackcrossingIntrogressionOryza sativaBiologyMarker-assisted selectionGrain qualityBiotechnologyBackground selectionGeneticsMolecular markerGeneGenetic markerAgronomy

Abstract

fetched live from OpenAlex

While it is crucial for developing countries like Malaysia to achieve self-sufficiency in rice (Oryza sativa L.), it is equally critical to be able to produce high-quality rice, specifically fragrant rice, which demands are often met through importation. The present study was aimed at developing high-yielding fragrant rice, in a timely and cost-effective manner. A marker-assisted backcross (MABC) approach was optimised to introgress the fragrance gene (fgr) into two high-yielding Malaysian varieties, MR84 and MR219, within two years utilising less than 50 molecular markers. Coupled with phenotypic screening, one single foreground marker (fgr-SNP) and 48 background markers were selected and utilised, revealing recovery of at least 90% of recurrent parent genome (RPG) in merely two backcross generations. Collectively, the yield potential of the developed BC 2 F 2 lines (BLs) was higher (P &gt; 0.05) than the donor parent, MRQ74, and similar (P &lt; 0.05) to both the recurrent parents, MR84 and MR219. In addition, some of the developed BLs showed good grain quality, such as having long grain. We believe that this is the first report comprising the validation and utilisation of the single functional marker system (fgr-SNP) in introgressing the fgr gene into different rice varieties.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.392

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 studies0.0010.000
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 head0.038
GPT teacher head0.277
Teacher spread0.239 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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