SALE OF EXTRA VIRGIN OLIVE OIL IN THE WORLD
Bibliographic record
Abstract
Olive farming in Brazil, occupying around seven thousand hectares, mainly in the South and Southeast regions, reveals a national market that is still incipient, with an annual per capita consumption of just 0.4 liters, contrasting with 13 liters in Greece.Despite this, the country shows potential for growth as a major importer of olive oil.Rio Grande do Sul emerges as the largest national producer, registering a significant increase in its production in the last five years.Globally, the demand for healthy products drives the external olive oil market, highlighting Brazilian participation in the International Agreement on olive oil and table olives in 2015.In 2023, the main destination markets included the United States, European Union, Brazil, Japan, Canada, China and Australia, representing 80% of world exports.The main olive oil exporting countries are Spain, followed by Portugal, the United States, Morocco, Tunisia and Turkey.For Brazil to become a major olive producer, it is crucial to provide access to technical information and encourage investments in olive farming.The commercialization of olive oil presents challenges and opportunities for the sector, making it essential to establish public policies and develop effective strategies for producers and exporters of extra virgin olive oil.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".