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Record W2912244431 · doi:10.5376/mpb.2019.10.0002

Precisely Positioning QTLs for Premature Senescence Resistance in Asparagus Bean Using a High-density SNP Chip

2019· article· en· W2912244431 on OpenAlexvenueno aff
Lijuan Huang, Xilin Yuan, Xinyi Wu, Ying Wang, Xiaohua Wu, Baogen Wang, Zhongfu Lu, Guojing Li, Pei Xu

Bibliographic record

VenueMolecular Plant Breeding · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyAsparagusSNPSNP genotypingSenescenceResistance (ecology)GeneticsBiotechnologyBotanyGeneGenotypeSingle-nucleotide polymorphismAgronomy

Abstract

fetched live from OpenAlex

Premature senescence (PS) is an important adverse agronomic trait of asparagus bean. Since immature pods are the major economic organ for asparagus bean, PS will influence the pod number in late stage which leads to the decline of pod yield and brings economic losses to growers. Using an enlarged recombinant inbred line population (RIL, F6:8) comprising 119 lines from the cross of ZN016 and ZJ282 as materials, the study carried out the quantitative trait loci (QTL) precise positioning of PS resistance traits of asparagus bean based on the high density molecular genetic map with 8,032 SNP loci which was constructed by the 60K high-density SNP chip of cowpea. We positioned the major QTLs for PS resistance of asparagus bean to an about 0.424 cM interval on LG11. Compared with previous results of QTL positioning of the same trait, the coarse positioning results in the early stage of this laboratory were confirmed, and the accuracy of QTL positioning was greatly improved which greatly reduced the distance between the wing markers. This study might provide the basis for molecular marker-assisted breeding and gene cloning against the agronomic trait of PS resistance.

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 score0.926
Threshold uncertainty score0.397

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 head0.023
GPT teacher head0.213
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

Citations0
Published2019
Admission routes1
Has abstractyes

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