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Record W4396524725 · doi:10.5376/rgg.2024.15.0008

Molecular Identification and Breeding Strategies of Rice Blast Resistance Genes

2024· article· en· W4396524725 on OpenAlexvenueno aff
Jianru Yang

Bibliographic record

VenueRice Genomics and Genetics · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsIdentification (biology)GeneBiologyResistance (ecology)GeneticsBiotechnologyMolecular breedingComputational biologyAgronomyBotany

Abstract

fetched live from OpenAlex

The harm of rice blast and the importance of rice have always received widespread attention. This review aims to explore the molecular identification and breeding strategies of rice blast resistance genes, providing a detailed overview. It elaborates on the methods for identifying blast resistance genes, including past genetic research and the application of modern molecular biology technology, as well as molecular breeding strategies for blast resistance. It emphasizes the importance of molecular marker assisted breeding of resistant varieties. This review provides detailed information on molecular breeding methods, showcasing developed blast resistant rice varieties and their applications in different regions. This is one of the main results, combined with current challenges and future prospects, to help readers understand the future direction of this field. The article summarizes the importance of molecular identification of blast resistant genes in rice and molecular breeding strategies for global food security, And how to address future challenges, this topic provides a theoretical basis for future research and decision-making.

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 score0.473
Threshold uncertainty score0.179

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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