Bibliographic record
Abstract
Abstract Citrus fruits are among the most popular fruits and have a long history of production and use. However, within the past century, industrial technologies have began to develop in order to convert citrus fruits into commercial products. Citrus has proven to be a very good option for oil and essence production. Citrus peel oils are of a very complex composition and are contained in oval, balloon‐shaped oil sacs, or vesicles locate in the outer rind, or flavedo, of the fruit The oil is usually extracted by mechanical separation or hydrodistillation.Citrus seeds are regarded as a new source of oil. The seed oil is recovered from the seeds by crushing and solvent extraction. Citrus essences are distilled aqueous solutions of more volatile components from the corresponding citrus juices.Citrus peel oils are used widely in beverages, cosmetics, pharmaceuticals, and the perfumery industry. Seed oils are used in cooking and the treatment of leather and textiles. Quality and freshness are the major considerations pertaining to their value and applications. Most of the qualitative changes in citrus peel oil occur during storage.Storage changes and chemical composition of the citrus oils are described. Despite increasing application of these oils, certain challenges related to potential health‐damaging properties and contamination exist. These health and safety factors are discussed.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".