The impact of climate change on mycotoxin contamination in cereal grains and the implications for children’s health in Canada
Bibliographic record
Abstract
Climate change is currently the most pressing environmental concern, especially for northern climatic regions like Canada. Climate change impacts a wide variety of environmental factors that in turn alter vegetative processes, like that in cereal grains. As grain kernels weaken due to environmental stress it becomes increasingly susceptible to infection. This review will detail one such type of infection produced by fungi: mycotoxins. Mycotoxins come in several varieties of which five will be examined in this review: aflatoxin, ochratoxin, deoxynivalenol, fumonisins, and zearalenone. Mycotoxins cause many different types of illnesses ranging from gastrointestinal disruption to death. Since mycotoxins affect plants, all consumers are at a possible risk of infection, with the most vulnerable members of the population being children, due to their small body size. Therefore, this review will assess the impact of climate change on mycotoxin contamination in cereal grains and the implications for children’s health in a Canadian context.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".