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Record W4415593267 · doi:10.1080/15592324.2025.2568929

Epigenetic control of seed development and dormancy in cereals

2025· review· en· W4415593267 on OpenAlexafffund
Manjit Singh, Karminderbir Kaur, Purnima Kandpal, Zhou Zhou, Weiyuan Chen, Jaswinder Singh

Bibliographic record

VenuePlant Signaling & Behavior · 2025
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDormancyGerminationSeed dormancyEpigeneticsSproutingCropDNA methylation

Abstract

fetched live from OpenAlex

Seeds, which are imperative for the propagation of seed plants, are also of major nutritional and economic value in agriculture. The precise dormancy and germination of crop seeds are important traits for modern agriculture. Pre-harvest sprouting (PHS) or the germination of seeds while attached to the plant before harvest, is a significant problem in crops, particularly in cereals. Therefore, understanding the various mechanisms of seed development and dormancy are imperative. While the molecular and hormonal aspects of seed dormancy are well understood, the role of epigenetic pathways is just beginning to unravel, particularly for cereal crops. The majority of this information has been generated in Arabidopsis; however, there is increasing focus on cereal crops such as rice and maize. Other important cereal crops, such as wheat and barley, lag behind even though seed dormancy and PHS are even more critical for these crops. Similarly, while much progress has been made in understanding the role of histone modifications in seed development, the role of DNA methylation has not been well investigated. In this article, we review the progress made in uncovering the role of epigenetic modifications in cereal crops with reference to knowledge generated in Arabidopsis.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.043
GPT teacher head0.286
Teacher spread0.243 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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