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Record W4404010829 · doi:10.14601/phyto-11275

Grapevine trunk disease fungi: their roles as latent pathogens and stress factors that favour disease development and symptom expression

2020· article· en· W4404010829 on OpenAlexaff
Jared Hrycan, Miranda M. Hart, Pat Bowen, T. Forge

Bibliographic record

VenueCharles Sturt University Research Output (CRO) · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsDiseaseBiologyExpression (computer science)TrunkMicrobiologyMedicineBotanyInternal medicineComputer science

Abstract

fetched live from OpenAlex

Grapevine trunk diseases (GTDs) are major biotic factors reducing yields and limiting vineyard economic life spans. Fungi in the GTD complex cause a range of symptoms in host plants, although these pathogens are slow wood colonizers and potentially latent pathogens. Understanding has recently increased on the possible roles that GTD fungi may play as latent pathogens, and how this can be translated into disease management. This paper summarizes evidence for the latent nature of infections by these fungi in grapevines and other hosts. Abiotic and biotic stressors have been associated with symptom expression in many hosts, but limited information is available regarding their roles in symptom development in grapevines. Based on research conducted in other pathosystems, this review discusses how abiotic and/or biotic stress factors may influence the transition from the endophytic to the pathogenic phases for GTD fungi. Potential methods for stress mitigation are also outlined as alternative GTD control strategies to minimize the economic impacts that that these diseases have on grape production.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.245
Teacher spread0.196 · 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
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

Citations72
Published2020
Admission routes1
Has abstractyes

Explore more

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