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Record W4285008998 · doi:10.3390/jof8070719

Identification and Characterization of Calonectria Species Associated with Plant Diseases in Southern China

2022· article· en· W4285008998 on OpenAlexaff
Yunxia Zhang, Cantian Chen, Chao Chen, Jingwen Chen, Meimei Xiang, Dhanushka N. Wanasinghe, Tom Hsiang, Kevin D. Hyde, Ishara S. Manawasinghe

Bibliographic record

VenueJournal of Fungi · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsUniversity of Guelph
FundersNational Natural Science Foundation of China
KeywordsBiologyBotanyPhylogenetic tree

Abstract

fetched live from OpenAlex

Calonectria species are important plant pathogens on a wide range of hosts, causing significant losses to plant production worldwide. During our survey on phytopathogenic fungi from 2019 to 2021, diseased samples were collected from various hosts in Guangdong Province, China. In total, 16 Calonectria isolates were obtained from leaf spots, stem blights and root rots of species of Arachis, Cassia, Callistemon, Eucalyptus, Heliconia, Melaleuca and Strelitzia plants. Isolates were identified morphologically, and a multigene phylogenetic analysis of combined partial sequences of calmodulin (cmdA), translation elongation factor 1-alpha (tef1-α) and beta-tubulin (β-tubulin) was performed. These sixteen isolates were further identified as nine Calonectria species, with five new species: Ca. cassiae, Ca. guangdongensis, Ca. melaleucae, Ca. shaoguanensis and Ca. strelitziae, as well as four new records: Ca. aconidialis from Arachis hypogaea, Ca. auriculiformis from Eucalyptus sp., Ca. eucalypti from Callistemon rigidus, and Ca. hongkongensis from Eucalyptus gunnii. Moreover, we provide updated phylogenetic trees for four Calonectria species complexes viz. Ca. colhounii, Ca. cylindrospora, Ca. kyotensis and Ca. reteaudii. Our study is the first comprehensive study on Calonectria species associated with various hosts from subtropical regions in China. Results from the present study will be an addition to the biodiversity of microfungi in South China.

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 score0.898
Threshold uncertainty score0.155

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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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