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Record W2028344239 · doi:10.1139/g06-101

Population structure of Sclerotinia sclerotiorum in an Australian canola field at flowering and stem-infection stages of the disease cycle

2006· article· en· W2028344239 on OpenAlexvenueno aff
Adrienne Sexton, Athol R. Whitten, Barbara J. Howlett

Bibliographic record

VenueGenome · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
FundersGrains Research and Development Corporation
KeywordsBiologySclerotinia sclerotiorumStem rotPopulationAscocarpAscosporeCanolaAnalysis of molecular varianceBotanyGenotypeGeneticsHaplotypeGene

Abstract

fetched live from OpenAlex

Populations of the ascomycete pathogen Sclerotinia sclerotiorum sampled from a canola field were analysed using microsatellite markers. Fifty isolates were collected from ascospore-infested canola petals and, later in the season, another 55 isolates were obtained from stem lesions; these isolates were used to compare inoculum and disease-causing populations. Fifty-five unique haplotypes were identified, with gene diversity ranging from 0.40 to 0.71. Genotypic diversity was higher in the inoculum population than it had been in the previous year, but analysis of molecular variance (AMOVA) showed that less than 10% of the variation was attributable to differences between the 2 years. Genotypic disequilibrium measures were consistent with the occurrence of both clonal reproduction and out-crossing. There was no significant population subdivision between the ascospore and stem-lesion populations, as measured with fixation indices (R(ST) = 0.015, p = 0.90) and AMOVA, suggesting that there are no genetically defined subgroups of isolates more likely to proceed from petal colonization to cause stem infection. This might be because S. sclerotiorum possesses wide-ranging pathogenicity mechanisms that account for the lack of host specificity observed to date.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.991

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 head0.011
GPT teacher head0.194
Teacher spread0.183 · 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 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

Citations60
Published2006
Admission routes1
Has abstractyes

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