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Record W2760503044

Novel insights into the elm yellows phytoplasma genome and into the metagenome of elm yellows-infected elms

2017· article· en· W2760503044 on OpenAlexaboutno aff
Christina Rosa, Paolo Margaria, Scott M. Geib, Erin D. Scully

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytoplasmas and Hemiptera pathogens
Canadian institutionsnot available
Fundersnot available
KeywordsDutch elm diseaseBiologyLeafhopperPhytoplasmaUlmus pumilaBotanyGeneGeneticsPolymerase chain reactionHemiptera
DOInot available

Abstract

fetched live from OpenAlex

In North America, American elms were historically present throughout the northeastern United States and southeastern Canada. The longevity of these trees, their resistance to the harsh urban environment, and their aesthetics led to their wide use in landscaping and streetscaping over several decades. American elms were one of most cultivated plants in the United States until the arrival of Dutch elm disease (DED) and elm yellows disease (EY). EY epidemics have killed large numbers of elm trees in the northeastern United States beginning in the 1940s. Since then, the disease has gradually been spreading to the southern and western regions of the United States while remaining endemic in the Northeast. Today EY, together with DED, is responsible for the death of most of the American species of elm trees, including (Ulmus americana (L.), U. rubra (Muh.), U. alata (Michx.), U. crassifolia (Nutt.) U. serotina (Sarg)) and of some of their natural hybrids (i.e. U. pumila × rubra). We performed next-generation sequencing on EY-infected elm trees to discover EY effector genes involved in plant-phytoplasma interactions and to survey the metagenome of the infected elms. This research is a basic step to understand how EY infection shapes the elm microbial communities and, in the long term, will lead to a better understanding of the pathogenesis of EY infection in elm and the interactions between EY and its leafhopper vectors.

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 categoriesScience and technology studies
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 score0.908
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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