MétaCan
Menu
Back to cohort

Genetic mechanisms, biological function, and biotechnological advance in sorghum tannins research

2025· review· en· W4409054114 on OpenAlexaff
Fu Wang, Qian Zhao, Shuyao Li, Ruidong Sun, Zhenyuan Zang, Ai‐Sheng Xiong, El Hadji Seck, Yu‐Xin Ye, Jian Zhang

Bibliographic record

VenueBiotechnology Advances · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsUniversity of British Columbia, Okanagan Campus
FundersHigher Education Discipline Innovation ProjectJiangsu Provincial Department of Human Resources and Social SecurityJilin Agricultural UniversityPeople's Government of Jilin Province
KeywordsSorghumBiotechnologyFunction (biology)BiologySorghum bicolorBusinessAgronomyEvolutionary biology

Abstract

fetched live from OpenAlex

Sorghum (Sorghum bicolor) holds a unique position in the human diet and serves as a stable food source in many developing countries especially in African and south Asian regions. Tannins, the primary secondary metabolites in sorghum, are pivotal in determining its characteristic bitter taste. Beyond their influence on flavor, tannins play a vital role in sorghum's resistance to biotic and abiotic stresses and serve as key indicators of grain quality. The concentration of tannins significantly affects the potential for diverse applications of sorghum. This review provides a comprehensive analysis of sorghum tannins, focusing on their genetic basis, biological activities, and biosynthesis mechanisms. It highlights the relationship between tannin levels and grain color and delves into the underlying biogenetic pathways. Furthermore, the potential of functional genomics and biotechnological approaches in precisely controlling tannin levels for sorghum breeding is discussed. This study aims to offer valuable insights and perspectives for advancing both the scientific understanding and practical applications of sorghum tannins.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0040.001
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 head0.038
GPT teacher head0.366
Teacher spread0.329 · 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.

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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBiotechnology AdvancesSame topicPlant Gene Expression AnalysisFrench-language works237,207