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Record W3206619387 · doi:10.3390/genes12101635

ABA and Bud Dormancy in Perennials: Current Knowledge and Future Perspective

2021· review· en· W3206619387 on OpenAlexafffund
Wenqiang Pan, Jiahui Liang, Juanjuan Sui, Jingru Li, Chang Liu, Yanmin Zhang, Shaokun Wang, Yajie Zhao, Jie Zhang, Mingfang Yi, Sonia Gazzarrini, Jian Wu

Bibliographic record

VenueGenes · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Anhui ProvinceHigher Education Discipline Innovation ProjectNatural Science Foundation of Beijing MunicipalityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsDormancyBiologyEpigeneticsPerennial plantTraitHormoneRegulatorPhysiologyAdaptation (eye)BotanyGeneticsNeuroscienceGeneGerminationBiochemistryComputer science

Abstract

fetched live from OpenAlex

Bud dormancy is an evolved trait that confers adaptation to harsh environments, and affects flower differentiation, crop yield and vegetative growth in perennials. ABA is a stress hormone and a major regulator of dormancy. Although the physiology of bud dormancy is complex, several advancements have been achieved in this field recently by using genetics, omics and bioinformatics methods. Here, we review the current knowledge on the role of ABA and environmental signals, as well as the interplay of other hormones and sucrose, in the regulation of this process. We also discuss emerging potential mechanisms in this physiological process, including epigenetic regulation.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.070
GPT teacher head0.340
Teacher spread0.270 · 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

Citations95
Published2021
Admission routes2
Has abstractyes

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