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Record W3118426610 · doi:10.3389/fpls.2020.606918

The Molecular Regulation of Carbon Sink Strength in Grapevine (Vitis vinifera L.)

2021· review· en· W3118426610 on OpenAlexaff
Youmei Li, Charles F. Forney, Bhaskar Bondada, Feng Leng, Zhaosen Xie

Bibliographic record

VenueFrontiers in Plant Science · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of China
KeywordsSugarSink (geography)SucrosePhotosynthesisVitis viniferaBotanyBiologyChemistryAgronomyHorticultureFood science

Abstract

fetched live from OpenAlex

Sink organs, the net receivers of resources from source tissues, provide food and energy for humans. Crops yield and quality are improved by increased sink strength and source activity, which are affected by many factors, including sugars and hormones. With the growing global population, it is necessary to increase photosynthesis into crop biomass and yield on a per plant basis by enhancing sink strength. Sugar translocation and accumulation are the major determinants of sink strength, so understanding molecular mechanisms and sugar allocation regulation are conducive to develop biotechnology to enhance sink strength. Grapevine ( Vitis vinifera L. ) is an excellent model to study the sink strength mechanism and regulation for perennial fruit crops, which export sucrose from leaves and accumulates high concentrations of hexoses in the vacuoles of fruit mesocarp cells. Here recent advances of this topic in grape are updated and discussed, including the molecular biology of sink strength, including sugar transportation and accumulation, the genes involved in sugar mobilization and their regulation of sugar and other regulators, and the effects of hormones on sink size and sink activity. Finally, a molecular basis model of the regulation of sugar accumulation in the grape is proposed.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.282
Teacher spread0.254 · 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 designOther design
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

Citations45
Published2021
Admission routes1
Has abstractyes

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