Applications of Essential Oils and Plant Extracts in Different Industries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Essential oils (EOs) and plant extracts are sources of beneficial chemical compounds that have potential applications in medicine, food, cosmetics, and the agriculture industry. Plant medicines were the only option for preventing and treating mankind's diseases for centuries. Therefore, plant products are fundamental sources for producing natural drugs. The extraction of the EOs is the first important step in preparing these compounds. Modern extraction methods are effective in the efficient development of these compounds. Moreover, the compounds extracted from plants have natural antimicrobial activity against many spoilage and disease-causing bacteria. Also, the use of plant compounds in cosmetics and hygiene products, in addition to their high marketability, has been helpful for many beauty problems. On the other hand, the agricultural industry has recently shifted more from conventional production systems to authenticated organic production systems, as consumers prefer products without any pesticide and herbicide residues, and certified organic products command higher prices. EOs and plant extracts can be utilized as ingredients in plant antipathogens, biopesticides, and bioherbicides for the agricultural sector. Considering the need and the importance of using EOs and plant extracts in pharmaceutical and other industries, this review paper outlines the different aspects of the applications of these compounds in various sectors.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it