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Record W4400416670 · doi:10.1186/s43088-024-00524-7

Metabolic engineering of terpene metabolism in lavender

2024· article· en· W4400416670 on OpenAlexafffund
Ojo Michael Oseni, Reza Sajaditabar, Soheil S. Mahmoud

Bibliographic record

VenueBeni-Suef University Journal of Basic and Applied Sciences · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLavenderLamiaceaeCosmeticsTerpeneLavender oilEssential oilMetabolic engineeringChemistryTraditional medicineBotanyBiologyMedicineBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Background: Several members of the Lamiaceae family of plants produce large amounts of essential oil [EO] that find extensive applications in the food, cosmetics, personal hygiene, and alternative medicine industries. There is interest in enhancing EO metabolism in these plants. Main body: class of the isoprenoids or terpenoids. In recent years, substantial effort has been made by researchers to study terpene metabolism and enhance lavender EO through plant biotechnology. This paper reviews recent advances related to the cloning of lavender monoterpene biosynthetic genes and metabolic engineering attempts aimed at improving the production of lavender monoterpenes in plants and microbes. Conclusion: Metabolic engineering has led to the improvement of EO quality and yield in several plants, including lavender. Furthermore, several biologically active EO constituents have been produced in microorganisms.

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 score0.029
Threshold uncertainty score0.242

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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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