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Multiple gene genealogies reveal important relationships between species of<i>Phaeophleospora</i>infecting<i>Eucalyptus</i>leaves

2007· article· en· W2143197189 on OpenAlexfundno aff
Vera Andjic, G.E.St.J. Hardy, María Noel Cortinas, Michael J. Wingfield, Treena I. Burgess

Bibliographic record

VenueFEMS Microbiology Letters · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaMurdoch UniversityUniversity of PretoriaAustralian Research CouncilUniversité Laval
KeywordsMycosphaerellaEucalyptusBiologySubtropicsQuarantineBotanyPhylogenetic treeBiosecurityGeneEcologyGenetics

Abstract

fetched live from OpenAlex

The majority of Eucalyptus species are native to Australia, but worldwide there are over 3 million ha of exotic plantations, especially in the tropics and subtropics. Of the numerous known leaf diseases, three species of Phaeophleospora can cause severe defoliation of young Eucalyptus; Phaeophleospora destructans, Phaeophleospora eucalypti and Phaeophleospora epicoccoides. Phaeophleospora destructans has a major impact on seedling survival in Asia and has not, as yet, been found in Australia where it is considered a serious threat to the biosecurity of native eucalypts. It can be difficult to distinguish Phaeophleospora species based on symptoms and micromorphology and an unequivocal diagnostic tool for quarantine purposes would be useful. In this study, a multiple gene genealogy of these Phaeophleospora species and designed specific primers has been constructed to detect their presence from leaf samples. The phylogenetic position of these Phaeophleospora species within Mycosphaerella was established. They are closely related to each other and to other important Eucalyptus pathogens, Mycosphaerella nubilosa, Mycosphaerella cryptica and Colletogloeopsis zuluensis. The specific primers developed can now be used for diagnostic and screening purposes within Australia.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.222
Teacher spread0.204 · 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.

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

Citations34
Published2007
Admission routes1
Has abstractyes

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