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Record W4292604149 · doi:10.32615/ps.2022.037

Comparative study of photosynthetic capacity in lower leaves in the canopy of dwarf and semidwarf wheat

2022· article· en· W4292604149 on OpenAlexaff
Huandong Li, J. ZHANG, Qi Zheng, Bao-Long LI, Zongzheng Li

Bibliographic record

VenuePhotosynthetica · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsInstitute of Genetics
FundersNational Natural Science Foundation of China
KeywordsCanopyPhotosynthesisBiologyBotanyPhotosynthetic capacityPoaceaeAgronomy

Abstract

fetched live from OpenAlex

Semidwarf wheat contributes to significant yield increase worldwide, however, few dwarf cultivars of wheat are cultivated due to grain yield penalty. In this study, a new dwarf wheat Triticum aestivum L., cv. Xiaoyan 101, was investigated to explore its photosynthetic performance and yield potential. In the comparison of the semidwarf wheat cultivars, Jing 411 and Xiaoyan 101, although the first three leaves (including flag leaves) did not differ significantly in both genotypes, Xiaoyan 101 conferred a higher content of photosynthetic pigments and higher photochemical efficiency but had lower contents of hydrogen peroxide and malondialdehyde in lower leaves in the canopy. In addition, the antioxidant enzymes-encoding genes were upregulated while the senescence-associated genes (TaSAG3, TaSAG5, TaSAG7, and TaSAG12) were downregulated in lower leaves in the canopy of Xiaoyan 101. Ultimately, Xiaoyan 101 produced approximate or even higher grain yield than the local semidwarf wheat varieties. Therefore, it is possible to breed dwarf wheat with enhanced photosynthetic activity but without yield sacrifice.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.054
GPT teacher head0.246
Teacher spread0.192 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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