Ukraine as a food and grain hub: Impact of science and technology development on food security in the world
Bibliographic record
Abstract
The challenges facing the world today caused by a growing population, reduced resources, global warming, climate shocks, and social and political crises are heavily affecting agri-food systems and supply chains. A global food crisis fueled by conflicts, global warming, climate shocks, and the COVID-19 pandemic is growing because of the bad effects of the war in Ukraine which is one of the world’s major breadbaskets. Science and innovation are the key accelerators to achievingthe complex rapid change in food production, distribution, and consumption required to support the global food security. This article reviews the information on grains, crops, and food production in Ukraine and discusses how the development of food education, science, and technology in Ukraine may impact food security in the world. Ukrainian food science as a part of the global scientific community offers solutions to enhance the stability of the grain and food supply while aiding to reduce food and grain loss, improve food safety, develop novel processing technologies such as pulsed electric field technology (PEF), biotechnology, and extraction methods for biomass recovery or separation technologies, increase environmental safety, energy saving, management of food production and distribution, make advancement in the production of sugar and alcohol, and improvements of food attributes. In support of this conclusion, the main research and development achievements of Ukrainian food scientists are represented.
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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 | 0.000 | 0.000 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".