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
Abstract Citrus fruits are among the most popular fruits nowadays and have a very long history of production and use. However, within the past century, industrial technologies began to develop in order to convert citrus fruits into commercial products. Each year, millions of tons of citrus fruits are delivered to factories for processing and juice production. Historically, the oldest citrus product is the oil. In ancient Sicily, where early Italian citrus industry had just been introduced, lemons were primarily grown for production of lemon oil, and juice was treated as a waste product until its later use for citric acid recovery. The early use of lemon and orange oils was mainly in perfumery and pharmaceuticals. With rapid development of science and technology, more areas of use of citrus oils were found, for which more detailed information on chemical composition and properties were required. The modern perfume and flavor industries have benefited from further research on citrus peel oil and essence. Besides, the yield of citrus seed oil has increased since citrus seeds were discovered as a new source of edible oil. In this article, the chemical composition, properties, and uses of citrus oils and essences are provided.
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 | 0.001 |
| 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