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Record W4389228574 · doi:10.5423/ppj.rw.09.2023.0132

The Rapid Apple Decline Phenomenon: Current Status and Expected Associated Factors in Korea

2023· review· en· W4389228574 on OpenAlexaboutno aff
Seung‐Yeol Lee, Kari A. Peter, Kallol Das, Avalos-Ruiz Diane, Hee–Young Jung

Bibliographic record

VenueThe Plant Pathology Journal · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
FundersRural Development Administration
KeywordsRootstockAbiotic componentCultivarBiologyBiotic componentHorticultureBotanyEcology

Abstract

fetched live from OpenAlex

Rapid apple decline (RAD) is a complex phenomenon affecting cultivated apple trees and particularly dwarf rootstocks on grafted young apple trees. Since its first appearance in the United States, RAD has been reported worldwide, for example in Canada, South America, Africa, and Asia. The phenomenon has also been observed in apple orchards in Korea, and it presented similar symptoms regardless of apple cultivar and cultivation period. Most previous reports have suggested that RAD may be associated with multiple factors, including plant pathogenic infections, abiotic stresses, environmental conditions, and the susceptibility of trees to cold injury during winter. However, RAD was observed to be more severe and affect more frequently apple trees on the Malling series dwarf rootstock. In this study, we reviewed the current status of RAD worldwide and surveyed biotic and abiotic factors that are potentially closely related to it in Korea.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.303
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

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