Mycotoxins that affect the North American agri-food sector: state of the art and directions for the future
Bibliographic record
Abstract
This paper summarises workshop discussions at the 5 th international MYCORED meeting in Ottawa, Canada (June 2012) with over 200 participants representing academics, government and industry scientists, government officials and farming organisations (present in roughly equal proportions) from 27 countries. Workshops centred on how mycotoxins in food and feed affect value chains and trade in the region covered by the North American Free Trade Agreement. Crops are contaminated by one or more of five important mycotoxins in parts of Canada and the United States every year, and when contaminated food and feed are consumed in amounts above tolerable limits, human and animal health are at risk. Economic loss from such contamination includes reduced crop yield, grain quality, animal productivity and loss of domestic and export markets. A systematic effort by grain producers, primary, transfer, and terminal elevators, millers and food and feed processers is required to manage these contaminants along the value chain. Workshops discussed lessons learned from investments in plant genetics, fungal genomics, toxicology, analytical and sampling science, management strategies along the food and feed value chains and methods to ameliorate the effects of toxins in grain on animal production and on reducing the impact of mycotoxins on population health in developing countries. These discussions were used to develop a set of priorities and recommendations.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".