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Record W4411883243 · doi:10.1038/s41597-025-05192-5

De novo Genome Assembly and Annotation of 12 Fungi Associated with Fruit Tree Decline Syndrome in ON, Canada

2025· article· en· W4411883243 on OpenAlexafffundabout
Mikhail G. Sulman, Evgeny Ilyukhin, Oscar Villanueva, Hai D. T. Nguyen, Shawkat Ali, Walid Ellouze

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaHospital for Sick ChildrenMinistry of Agriculture, Food and Rural AffairsMcGill UniversityGénome QuébecOntario Ministry of Agriculture, Food and Rural AffairsGovernment of Canada
KeywordsBiologyGenomeAbiotic componentEcologyGeneGenetics

Abstract

fetched live from OpenAlex

Apple and stone fruit trees are vital components of Ontario's agricultural landscape. However, since 2016, these trees have been facing alarming mortality rates, exhibiting symptoms collectively referred to as Fruit Tree Decline (FTD) and Rapid Apple Decline (RAD). Despite its widespread occurrence, the exact cause of FTD and RAD remains elusive, with various pathogenic fungi and viruses implicated, along with abiotic stressors such as drought, winter injury and nutrient deficiency. In this study, we sequenced, assembled and annotated the genomes of 12 fungi associated with FTD and RAD syndromes in Ontario, Canada. We present the first and only publicly available assemblies for three ascomycete species including Diplodia intermedia, Diatrype stigma, and Nothophoma quercina. Additionally, we present high-quality reference genome sequences for Diplodia seriata, Didymella pomorum and Neofusicoccum ribis. These genomic resources are valuable for understanding the molecular mechanisms behind FTD and RAD, and for developing strategies for disease prevention and management in fruit trees.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.402
Threshold uncertainty score0.809

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.237
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreDataset

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
Published2025
Admission routes3
Has abstractyes

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