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Record W2045047245 · doi:10.1038/srep02876

Tomato yellow leaf curl virus alters the host preferences of its vector Bemisia tabaci

2013· article· en· W2045047245 on OpenAlexfundno aff
Yong Fang, Xiaoguo Jiao, Wen Xie, Shaoli Wang, Qingjun Wu, Xiaobin Shi, Gong Chen, Qi Su, Xin Yang, Huipeng Pan, Youjun Zhang

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersCanadian Forest ServiceNational Key Research and Development Program of ChinaNational Science Fund for Distinguished Young ScholarsU.S. Forest Service
KeywordsTomato yellow leaf curl virusWhiteflyBiologyHost (biology)Leaf curlBegomovirusPlant virusHorticultureVector (molecular biology)BotanyVirologyVirusGene

Abstract

fetched live from OpenAlex

Bemisia tabaci, the whitefly vector of Tomato yellow leaf curl virus (TYLCV), seriously reduces tomato production and quality. Here, we report the first evidence that infection by TYLCV alters the host preferences of invasive B. tabaci B (Middle East-Minor Asia 1) and Q (Mediterranean genetic group), in which TYLCV-free B. tabaci Q preferred to settle on TYLCV-infected tomato plants over healthy ones. TYLCV-free B. tabaci B, however, preferred healthy tomato plants to TYLCV-infected plants. In contrast, TYLCV-infected B. tabaci, either B or Q, did not exhibit a preference between TYLCV-infected and TYLCV-free tomato plants. Based on gas chromatography-mass spectrometry (GCMS)analysis of plant terpene volatiles, significantly more β-myrcene, thymene, β-phellandrene, caryophyllene, (+)-4-carene, and α-humulene were released from the TYLCV-free tomato plants than from the TYLCV-infected ones. The results indicate TYLCV can alter the host preferences of its vector Bemisia tabaci B and Q.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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 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

Citations120
Published2013
Admission routes1
Has abstractyes

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