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Record W3028035243

Using QTL-seq to mine genes related to petiole length of cabbage

2020· article· en· W3028035243 on OpenAlexvenueno aff
Xiang Tai, Xiaowei Zhu, Jinxiu Chen, Daguo Gu, Tianyue Bo

Bibliographic record

VenuePlant Gene and Trait · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsPetiole (insect anatomy)BiologyQuantitative trait locusCandidate geneCentimorganGeneGeneticsBotanyChromosomeGene mapping
DOInot available

Abstract

fetched live from OpenAlex

The length of the petiole determines the area of a single plant. It is great significance to mining genes related to the length of the petiole of cabbage. In this experiment, short-petiole of high-bred line 301 ( male parent )、long-petiole of high-bred line 294 (fe male parent ) and its F 2 were used as materials. Extreme trait materials were selected from F 2 (30 long-petiole and 30 short-petiole materials) were used to construct pools of DNA. The candidate genes related to the petiole length of cabbage were mining using combination of QTL-seq and SNP / InDel-index. In this study, six candidate genes controlling the petiole length were identified from genome of cabbage. Seven candidate genes were enriched in the three pathways of SKU5 protein coding, (XTH) coding and plant cell wall synthesis. This study provides a reference for cloning of genes related to petiole length in cabbage.

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 score0.614
Threshold uncertainty score0.168

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 head0.042
GPT teacher head0.225
Teacher spread0.184 · 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
Published2020
Admission routes1
Has abstractyes

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