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Record W4403949813 · doi:10.5376/mpb.2024.15.0022

Nucleotide Polymorphism in <i>Zea</i>: Patterns and Influences on Crop Traits

2024· article· en· W4403949813 on OpenAlexvenueno aff
Zhou Lan, Yan Bao, Shuling Wang, Weixin Zhong, Xiangrui Sun

Bibliographic record

VenueMolecular Plant Breeding · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
FundersPeople's Government of Jilin Province
KeywordsBiologySingle-nucleotide polymorphismGeneticsPolymorphism (computer science)CropZea maysBiotechnologyGenotypeAgronomyGene

Abstract

fetched live from OpenAlex

Nucleotide polymorphisms, particularly single nucleotide polymorphisms (SNPs), play a crucial role in the genetic diversity and trait development of maize ( Zea mays ). This study synthesizes current research on the patterns of nucleotide polymorphism in maize and its wild relatives, and their influence on crop traits. Studies have shown that maize exhibits a high level of nucleotide diversity, with significant variations between landraces and inbred lines. Population structure and genetic subdivision significantly affect the observed patterns of nucleotide polymorphism, which is crucial for understanding the genetic basis of important agronomic traits. Advances in sequencing technology have facilitated the discovery and application of SNPs in crop genetics, enabling more precise association analyses and marker-assisted breeding. Specific polymorphisms have been linked to key traits such as root development and adaptation to environmental conditions, highlighting their potential for crop improvement. This study underscores the importance of integrating genetic diversity studies with modern genomic tools to enhance our understanding of trait development and improve maize breeding strategies.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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 designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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