MétaCan
Menu
Back to cohort

Wheat Leaf Rust, Caused by <i>Puccinia triticina</i> , and Mitigation Through Host Genetic Resistance

2024· article· en· W4404246320 on OpenAlexaff
Brent McCallum, Colin W. Hiebert, Xiben Wang, Guus Bakkeren

Bibliographic record

VenuePlant Health Cases · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsRust (programming language)Host (biology)Resistance (ecology)Host resistanceWheat leaf rustBiologyAgronomyBotanyHorticultureComputer scienceGeneticsGeneVirulence

Abstract

fetched live from OpenAlex

Abstract The obligate fungal pathogen Puccinia triticina causes leaf rust symptoms on wheat worldwide. Epidemics of leaf rust occur during periods of mild weather with high relative humidity. The pathogen infects leaves, causing orange to red pustules, containing millions of urediniospores, reducing the photosynthetic area and causing desiccation and yield loss. Wind-borne urediniospores are produced in continuous asexual cycles on wheat plants. Genetic resistance is the primary control strategy. There are over 80 known leaf rust ( Lr ) resistance genes, and most are race-specific and are active from the seedling stage to maturity. A few Lr genes are nonrace-specific and confer partial resistance to leaf rust and other diseases at the adult plant stage. Combining Lr genes that confer seedling and adult plant resistance has been particularly successful and durable. Pathogen populations are genetically diverse, and races evolve continuously to evade recognition by Lr genes. This results in boom-and-bust cycles when new cultivars with race-specific Lr genes are resistant for some years, but become susceptible, due to evolution of the P. triticina population. Surveillance of P. triticina for the evolution of races helps to develop wheat cultivars with Lr genes that will be resistant to the most prevalent races. Information © The Authors 2024

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.228

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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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 designNot applicable
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

Explore more

Same venuePlant Health CasesSame topicWheat and Barley Genetics and PathologyFrench-language works237,207