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Record W4323904009 · doi:10.5376/pgt.2023.14.0002

Analysis of Main Characters of Rice Progenies by Injecting Sorghum DNA into Rice

2023· article· en· W4323904009 on OpenAlexvenueno aff
Guangxin Li, Guangyuan Wang, Xiaohui Yu, Qing Mei, Guopeng Wang

Bibliographic record

VenuePlant Gene and Trait · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
FundersShanxi Provincial Key Research and Development ProjectShanxi Academy of Agricultural Sciences
KeywordsGermplasmPanicleSorghumTiller (botany)BiologyAgronomySweet sorghumSowing

Abstract

fetched live from OpenAlex

In this study, the whole genome DNA of sorghum apomixes line SSA-1 (Shanxi sorghum apomict-1) was injected into two rice varieties of ‘Liaoyan 28’ and ‘Liaoyan 6’ using the pollen tube pathway method. The genetic characters of the progeny changed obviously. By self-selection and identification, 27 phenotypic stable D 4  generation lines were obtained. Through field planting, the phenotypes of agronomic traits such as heading date, tiller number, plant height, and 1 000-grain weight of the introduced lines were statistically analyzed. The results showed that the agronomic traits of the D 4  generation lines were widely varied, some D 4  generation lines were more inclined to donor sorghum in culm and panicle color, which indicated that the whole genome DNA of sorghum was successfully introduced into the receptor cells, and the expression of the receptor gene was affected. Compared with the recipient parents, the introduced lines showed significant variations in the main traits such as heading date, tiller number, plant height, grain number per ear, grain weight per ear, ear length, and thousand grain weights. Further analysis and selection of mutant lines is helpful to discover new germplasm materials and provide abundant germplasm resources for breeding new rice varieties.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.341

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 head0.010
GPT teacher head0.216
Teacher spread0.205 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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