Analysis of the impact of Russian embargo on the EU agricultural and food sector.
Bibliographic record
Abstract
The Russian government adopted a list of products that are banned for a period of one year from the EU, United States, Norway, Canada and Australia. These products cover almost all milk and dairy products, meat products fruits and vegetables, as well as fish and crustaceans. These restrictions put a serious pressure on the European agri-food sector because of the temporary loss of a significant commercial market and because of possible cascade effects leading to oversupply on the internal market given the volumes involved and the quantity of perishable products banned in full harvesting season. Some sectors and Member States are more heavily affected i.e. 31% of EU milk products export, 29% of fruits and vegetables export. The overall temporary restrictions currently applied by Russia potentially jeopardize 5 billion EUR worth of trade and affects the income of 9.5 million people in the EU working on the holdings most concerned.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".