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Record W4220874070 · doi:10.1038/s41467-022-29132-8

Aegilops sharonensis genome-assisted identification of stem rust resistance gene Sr62

2022· article· en· W4220874070 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsUniversity of Saskatchewan
FundersBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesResearch Councils UKGatsby Charitable FoundationTel Aviv UniversityUniversity of MinnesotaGordon and Betty Moore Foundation
KeywordsStem rustBiologyGenomeGeneGeneticsIdentification (biology)Resistance (ecology)Rust (programming language)Computational biologyBotanyComputer scienceAgronomy

Abstract

fetched live from OpenAlex

The wild relatives and progenitors of wheat have been widely used as sources of disease resistance (R) genes. Molecular identification and characterization of these R genes facilitates their manipulation and tracking in breeding programmes. Here, we develop a reference-quality genome assembly of the wild diploid wheat relative Aegilops sharonensis and use positional mapping, mutagenesis, RNA-Seq and transgenesis to identify the stem rust resistance gene Sr62, which has also been transferred to common wheat. This gene encodes a tandem kinase, homologues of which exist across multiple taxa in the plant kingdom. Stable Sr62 transgenic wheat lines show high levels of resistance against diverse isolates of the stem rust pathogen, highlighting the utility of Sr62 for deployment as part of a polygenic stack to maximize the durability of stem rust resistance.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.247
Teacher spread0.218 · 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