The Mycotox Charter: Increasing Awareness of, and Concerted Action for, Minimizing Mycotoxin Exposure Worldwide
Bibliographic record
Abstract
Mycotoxins are major food contaminants affecting global food security, especially in low and middle-income countries. The European Union (EU) funded project, MycoKey, focuses on “Integrated and innovative key actions for mycotoxin management in the food and feed chains” and the right to safe food through mycotoxin management strategies and regulation, which are fundamental to minimizing the unequal access to safe and sufficient food worldwide. As part of the MycoKey project, a Mycotoxin Charter (charter.mycokey.eu) was launched to share the need for global harmonization of mycotoxin legislation and policies and to minimize human and animal exposure worldwide, with particular attention to less developed countries that lack effective legislation. This document is in response to a demand that has built through previous European Framework Projects—MycoGlobe and MycoRed—in the previous decade to control and reduce mycotoxin contamination worldwide. All suppliers, participants and beneficiaries of the food supply chain, for example, farmers, consumers, stakeholders, researchers, members of civil society and government and so forth, are invited to sign this charter and to support this initiative.
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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".