Bibliographic record
Abstract
Plant essential oils are volatile compounds that have been widely used in perfumery, aromatherapy, cosmetics, and for flavoring food and drink, and to a lesser extent, been used in food preservation, in medicine and household cleaning products. Essential oils and their major components are generally recognized as safe (GRAS), and because of their diverse biological activities, are focus of many studies looking for alternative agents to control bacteria, fungi and viruses in foods, crops, humans and animals. Monoterpenoids and phenylpropanoids are the major and perhaps the most important components of the various essential oils. These natural products have been proven to be a good source of antibacterial agents, particularly against food-borne pathogenic bacteria such as Escherichia coli, and Salmonella typhimurium . Essential oils or their major components have the potential to play an important role in food safety. They may also be used to control fungal decay of food, extending the shelf life of fresh produce and contributing to safer food by inhibiting the growth and mycotoxins production of important food-, feed- and soil-borne plant fungal pathogens. The antiviral and antifungal activity of certain essential oils and their components against human diseases provide a safe alternative to the synthetic drugs, particularly in immunocompromised individuals.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".