Addressing another threat to food safety: Conflict
Bibliographic record
Abstract
Societal Impact Statement The conflict between Ukraine and Russia will negatively affect not only food security but also food safety. Crops produced in Ukraine and Russia are at little risk of contamination by mycotoxins such as aflatoxin. However, due to the conflict, wheat, maize, sunflower, and other crops that would have been produced in and exported from Ukraine will need to be produced somewhere else. If done in warm production areas, strategies will need to be implemented to prevent mycotoxin contamination, which has negative health, social, and economic impacts. Summary Conflicts across the globe affect food security and also have a heavy toll on food safety. Many of the areas affected by conflict are breadbaskets for multiple countries. When the production of staple crops is compromised by diverse conflicts, it becomes necessary to grow them somewhere else to satisfy local, regional, and/or international requirements. However, if that production is done in tropical and subtropical zones, it must be done incorporating strategies to prevent mycotoxin contamination, which has negative health, social, and economic impacts. Otherwise, increased production of susceptible crops in mycotoxin‐prone areas may augment the already occurring negative impacts, which are severe in the global south.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".