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Record W4394363946 · doi:10.6084/m9.figshare.20209518

Exogenous brassinosteroids promotes root growth, enhances stress tolerance, and increases yield in maize

2022· dataset· en· W4394363946 on OpenAlexaff
Hao Zhang, Dan Zhao, Ziyan Tang, Ying Zhang, Ke Zhang, Jingao Dong, Fengru Wang

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsYield (engineering)Stress (linguistics)AgronomyBiologyHorticultureChemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Brassinosteroids (BRs) regulate of maize (Zea mays L.) growth, but the underlying molecular mechanism remains unclear. In this study, we used a multi-disciplinary approach to determine how BRs regulate maize morphology and physiology during development. Treatment with the BRs promoted primary root the elongation and growth during germination, and the early development of lateral roots. BRs treatment during the middle growth stage increased the levels of various stress resistance factors, and enhanced resistance to lodging, likely by protecting the plant against stem rot and sheath rot. BRs had no significant effect on plant height during late growth, but it increased leaf angle and photosynthetic efficiency, as well as yield and quality traits. Our findings increase our understanding of the regulatory effects of BR on maize root growth and development and the mechanism by which BR improves disease resistance, which could further the potential for using BR to improve maize yield.

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)0.003
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.025
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.010

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 head0.019
GPT teacher head0.217
Teacher spread0.198 · 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
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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