Bibliographic record
Abstract
Avocado oil continues to be a high demand product and there is a growing market for both name brands and private labels. Since our study on evaluating the purity and quality of name brand avocado oil in 2020, some producers have made efforts to assure quality and lend support for standard establishment. However, the purity and quality of private labeled avocado oil have not been evaluated and are of a concern for many consumers. This study evaluates thirty-six private label samples throughout the US and Canada. Out of 29 refined samples, three met both quality and purity standards, 11 met quality standards and eight met current proposed purity standards; out of 7 unrefined samples, three met current proposed purity standards for avocado oil. Key markers such as an elevated stearic fatty acid value with an elevated delta-7-stigmastenol value were identified to help professional buyers make educated decisions on what oils to purchase. Low cost can indicate a higher probability for adulteration; however, high cost does not guarantee a pure sample of appropriate quality. Both purity and quality parameters should be used to label the avocado oil appropriately to ease consumer confusion and increase their confidence in the avocado oil category. This work also highlighted the importance of continuing to research avocado oil, to understand natural variables that affect chemical compositions of avocado oil and to establish standards that accommodate these variances while minimizing adulterations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".