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Record W3154598073 · doi:10.1139/cjb-2021-0019

Dynamics of sugars, endogenous hormones, and oil content during the development of <i>Camellia oleifera</i> fruit

2021· article· en· W3154598073 on OpenAlexvenueno aff
Qiling Song, Ke Ji, Wenjuan Mo, Linkai Wang, Le Chen, Ling Gao, Wenfang Gong, Deyi Yuan

Bibliographic record

VenueBotany · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsnot available
FundersChinese Academy of ForestryCentral South University of Forestry and TechnologyCentral South University
KeywordsCamellia oleiferaBiologyBiochemistrySugarFructoseFatty acidPyruvate kinaseFood scienceBotanyGlycolysisEnzyme

Abstract

fetched live from OpenAlex

Camellia oleifera Abel (‘Huashuo’) is an important woody oil species with high economic value. Low yield is the main factor restricting the rapid development of the tea-oil industry. The dynamics of sugars, oil, endogenous hormones, and the genes involved in these pathways, as well as the correlation with oil synthesis-related factors in Huashuo, were investigated. The results showed that oil content was positively correlated with sucrose and starch content but was negatively correlated with reducing sugar content. Pyruvate kinase (PK) and fructose-1,6-bisphosphate aldolase (FBA) of the glycolysis pathway could be beneficial for oil biosynthesis. Higher levels of acetyl CoA carboxylase (ACC), stearoyl-ACP desaturase (SAD), and WRINKLED1 (WRI1), as well as lower levels of omega-6 fatty acid desaturase 2 (FAD2) and lipoxygenase 5 (LOX5) could be responsible for the oil production. The higher expression of gibberellin 20 oxidase (GA20ox), lower expression of gibberellic acid insensitive (GAI), and the increase in 9-cis-epoxycarotenoid dioxygenase (NCED) at the mRNA level could contribute to oil accumulation. The results of this study could provide a reference for increasing the yield of plants related to Huashuo.

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 score0.021
Threshold uncertainty score0.330

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 head0.017
GPT teacher head0.201
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

Citations24
Published2021
Admission routes1
Has abstractyes

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