MétaCan
Menu
Back to cohort
Record W2078882029 · doi:10.1139/b10-034

Virulence and molecular polymorphisms of the wheat leaf rust fungus <i>Puccinia triticina</i> in Canada from 1997 to 2007

2010· article· en· W2078882029 on OpenAlexaffvenueabout
Xiben Wang, Guus Bakkeren, Brent McCallum

Bibliographic record

VenueBotany · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsVirulenceBiologyWheat leaf rustRust (programming language)Puccinia reconditaGenotypeGenetic diversityGeneticsPopulationBotanyGeneDemography

Abstract

fetched live from OpenAlex

Populations of Puccinia triticina , one of the casual agents of wheat leaf rust disease, in the pacific (British Columbia and Alberta), prairie (Manitoba and Saskatchewan), and eastern regions (Quebec and Ontario) of Canada from 1997 to 2007 were analyzed for virulence and genetic diversity by revealing expressed sequence tag derived simple sequence repeat (EST-SSR) polymorphisms. Since 1997, a significant shift in the virulence of P. triticina occurred across Canada. The diversity of P. triticina virulence phenotypes in Manitoba and Saskatchewan, as measured by Shannon and Simpson indexes, decreased due to the directional selection toward predominant virulence phenotypes, whereas it remained relatively constant in Quebec and Ontario. The clustering of P. triticina virulence phenotypes from 1997 to 2007 was similar to that found in previous years, and was correlated with virulence to leaf rust resistance genes Lr2a, Lr2c, and Lr17a. Distinct EST-SSR profiles were found in different groups of P. triticina virulence phenotypes based on virulence to Lr2a, Lr2c, and Lr17a. In addition, the population of P. triticina in Manitoba and Saskatchewan was different from that in Quebec and Ontario from 1997 to 2007, based on both virulence characteristics and EST-SSR genotypes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.497

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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

Citations27
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueBotanySame topicWheat and Barley Genetics and PathologyFrench-language works237,207