Correlation between the Greatest Agricultural Products Exporters to the EU: is Ukraine included?
Bibliographic record
Abstract
Due to the challenges we are experiencing nowadays, the importance of food security is gaining in its attention, making the subjects supplying agricultural production and ready-made food products more important and influential either economically or politically. The data under research are the agricultural products exports of Brazil, Canada, China, Ukraine, the United Kingdom and the United States to the European Union. The agricultural products are the goods from SITC (0+1) groups. The timeframe under analysis is eleven years – from 2012 to 2022 included. The purpose of the research is to assess whether the Ukrainian agricultural exports to the EU are correlated with the said exports of Brazil, Canada, China, the UK and the USA, and, if they are, how strong the correlation is. The comparative analysis of the dynamics, simple statistics, differences with the previous periods for the agricultural products exports of the analysed subjects to the EU was conducted. The trend lines for the analysed data during the given timeframe and two following years, were built using the appropriate function. The Pearson and Spearman correlation coefficients and their corresponding p-values were calculated and analysed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".