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Record W4224115446 · doi:10.21203/rs.3.rs-1533022/v1

Structural evolution is the key to paternal identification in salt-resistant soybean breeding

2022· preprint· en· W4224115446 on OpenAlexaff
Yongcai Lai, jingmei lu, Haifeng Yu, Guang Yang, Ning Cao, Ying Ma, Yingdong Bi, Jing Tao, Wei Gao, Lu Niu, Xia Li, Zi Chen, Ying Wang, Yan Li, Changbin Sun, Gang Li, Dongmei Wu, Cuijing Liu, Wei Zhang, Ying Zhang, Ming-chun Sun, Jinmeng Chu, Fu-cai Xia, Weichen Qi, Shuyan Wan, Maha M. Hassan, Mohammed Ali, Xinglin Du

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsScience North
FundersNational Natural Science Foundation of China
KeywordsBiologySalt (chemistry)Phylogenetic treeAgronomyGeneBiotechnologyCultivarGeneticsChemistry

Abstract

fetched live from OpenAlex

Abstract Soybean breeding shows that soybean resources are increasingly narrow due to the depletion of ancestral varieties. We collected salt-tolerant wild soybean samples and seeds from 23 areas affected by heavy salt stress. SEM was used for structural evolution, Plant Print identification, salt and alkali resistance physiology, salt resistance gene and tissue culture experiments. We report here the discovery of Chinese wild soybean salt gland is first reported in the world. The salt tolerance of new soybean varieties is greatly improved by breeding wild soybean with salt gland as the male parent. A batch of new soybean varieties resistant to salt were obtained. The experiment revealed that the diversity of salt tolerance function of wild soybean was closely related to stress tolerance physiology, stress tolerance gene and phylogenetic structure evolution. Our results encourage the use of salt-resistant wild soybeans as paternal breeding to avoid undesirable breeding cycles at the expense of soybean ancestral 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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.342
Teacher spread0.257 · 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 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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