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Record W4416436337 · doi:10.1270/jsbbs.25027

Identification and characterization of QTLs for brown planthopper resistance from wild rice, <i>Oryza rufipogon</i>

2025· article· en· W4416436337 on OpenAlexfundno aff
Hoang Nam Nguyen, Takashige Ishii, Sachiyo Sanada‐Morimura, Shao‐Hui Zheng, Daisuke Fujita

Bibliographic record

VenueBreeding Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersInstitute of GeneticsJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology
KeywordsBrown planthopperBackcrossingOryza rufipogonQuantitative trait locusInbred strainOryza sativaJaponicaPEST analysis

Abstract

fetched live from OpenAlex

The brown planthopper (Nilaparvata lugens (Stål); BPH) is a serious pest of rice (Oryza sativa L.). Host plant resistance is an effective means of controlling it; at least 46 BPH resistance genes have been identified. However, BPH can overcome resistance genes, so we need to detect more genes. To find new BPH resistance genes in Oryza rufipogon, we analysed quantitative trait loci (QTLs) associated with BPH resistance. Using 161 backcross recombinant inbred lines derived from O. rufipogon accession W0630, we identified eight loci associated with resistance. qBPH5 and qBPH6, on chromosomes 5 and 6, were validated in F2 and F2:3 populations derived from crosses between O. sativa japonica variety ‘Nipponbare’ and the inbred lines. BPH resistance of a line carrying both QTLs was higher than that of lines carrying just one. Pyramiding improved resistance and can be used in breeding BPH-resistant rice.

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 score0.002
Threshold uncertainty score0.004

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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