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Record W7164744317 · doi:10.62550/yz138147

Single and Mixed Viral Infection Reduced Growth and Photosynthetic Pigment Content, Damaged Chloroplast Ultrastructure and Enhanced Virus Accumulation in Oriental Lily (Lilium auratum cv. Sorbonne)

2014· article· W7164744317 on OpenAlexaff
Yubao Zhang, Zhongkui Xie, Ruoyu Wang, H. R. Kutcher, Yajun Wang, Zhihong Guo

Bibliographic record

VenueThe Philippine Agricultural Scientist · 2014
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsChloroplastPigmentChlorophyllVirusPhotosynthesisUltrastructureCarotenoidPhotosynthetic pigment

Abstract

fetched live from OpenAlex

Both single- and mixed-infection experiments were performed to study the effects of Lily mottle virus (LM0V) and Cucumber mosaic virus (CMV) on the growth, photosynthetic pigment content, chloroplast structure and virus accumulation in oriental lily (Lilium auratum L. cv. Sorbonne). Virus infection with LM0V significantly reduced plant height and leaf length and width. Mixed infection with LM0V and CMV caused significantly more severe effects on growth than single infection by LM0V. In leaves exposed to mixed infection, there was a decrease in content of chlorophyll a (chl a), chlorophyll b (chl b), and carotenoids (car) as well as in the chl a/b and chl (a+b)/ car ratios. In addition, mixed infection greatly damaged the chloroplast ultrastructure. Plants infected with both viruses exhibited a significantly greater accumulation of LM0V coat protein (CP) gene compared with the single infection by LM0V. These results clearly indicated that mixed infection by LM0V and CMV brought about more damage to the growth and photosynthetic apparatus of lily plant compared with single infection by LM0V.

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 score0.002
Threshold uncertainty score0.005

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 integrity0.0000.001
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 head0.031
GPT teacher head0.242
Teacher spread0.211 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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