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Record W4409289479 · doi:10.5539/jas.v17n5p55

Optimizing Vetiver Oil Yield and Quality: A Comprehensive Approach Integrating Traditional and Modern Extraction Techniques

2025· article· en· W4409289479 on OpenAlexvenueno aff
Hamidreza Zobeir, Bahram Asiabanpour

Bibliographic record

VenueJournal of Agricultural Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsnot available
FundersTexas State UniversityU.S. Department of Agriculture
KeywordsYield (engineering)Extraction (chemistry)Quality (philosophy)Computer scienceProcess engineeringBiochemical engineeringEnvironmental scienceEngineeringChemistryChromatographyMaterials science

Abstract

fetched live from OpenAlex

This research explores how traditional distillation can be combined with modern green extraction methods to improve both the yield and quality of vetiver oil. Researchers use an integrative approach to assess how root age together with cultivation methods and extraction processes influence oil production levels. The study methodically examines multiple variables including boiling time and solvent volume together with environmental effects through soil-based cultivation and aquaponic methods. Research shows that vetiver root age greatly influences oil production where roots aged between one and three years generate superior yields compared to older roots. The study identified solvent volume as a critical determinant of oil yield because 300 g of solvent produced maximum oil quantities. Despite testing boiling time and fan operation, neither demonstrated steady yield improvements. The ultrasonic extraction process failed to deliver anticipated outcomes which may stem from problems related to intensity settings, frequency parameters, or probe configuration. The research highlights optimizing various factors to enhance extraction efficiency while setting a foundation for further sustainable vetiver oil production studies.

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 score0.195
Threshold uncertainty score0.196

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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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
Published2025
Admission routes1
Has abstractyes

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