Bibliographic record
Abstract
Publisher Summary This chapter describes that olive oil, a food staple in the warmer regions around the Mediterranean Sea, is now becoming popular throughout Europe and in the United States, Canada, and other countries. This is due to its highly characteristic flavor but also to the promotion of the health benefits of Mediterranean dietary patterns. Olive oil contributes complex flavors that are reflected throughout the whole dish and adds body and depth to food. A good quality olive oil blends perfectly with the greens. Traditional vegetable dishes are prepared with seasonal vegetables, pulses, and grains. Although very old, these recipes contain wisely balanced ingredients and meet health criteria as defined by modern science. In addition to salads and cooking, olive oil is also used in marinades, pasta sauces, for preserving fish, cheese, sausage, and vegetables, for the preparation of breakfast toasts (tostada con aceite), as a dip for bread and in sweets, savory dishes, and home bread. The International Olive Oil Council in its Trade Standards (COI/T.15/NC no 325,2003) defined three positive attributes: bitter, fruity, and pungent and 11 negative attributes: fusty, musty, muddy, sediment winey-vinegary, rancid, heated or burnt, hay or woody, greasy, vegetable water, brine, and earthy regarding olive oil.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.523 | 0.346 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".