Monitoring of Fusarium Trichothecenes in Canadian Cereal Grain Shipments from 2010 to 2012
Bibliographic record
Abstract
A method involving dry grinding, rotary sample dividing, and gas chromatography-mass spectrometry was evaluated for the analysis of eight Fusarium trichothecenes in cereal grains. Processing of whole cereal grains by the method produced representative test portions for the analysis of deoxynivalenol (DON). Method validation data, as well as the successful participation in various international proficiency tests, demonstrated the analytical method produced accurate and precise results. The evaluated method was used to monitor DON, 3- and 15-acetyldeoxynivalenol, nivalenol (NIV), T-2 toxin, HT-2 toxin, diacetoxyscirpenol, and fusarenon-X in shipments of Canadian wheat, durum, barley, corn, rye, and oats transported between August 1, 2010, and July 31, 2012. DON was the most frequently measured trichothecene, found in 231 of the 303 samples at concentrations up to 2.34 mg/kg; NIV was the next most frequently observed trichothecene, but its occurrence was limited to barley. Concentrations of DON were significantly associated with wheat class and grade. The median DON concentration in durum (0.09 mg/kg) was lower than that for hard red spring (0.21 mg/kg). Lower grades of wheat also contained higher median concentrations of DON than higher grades, supporting the current use of Fusarium damaged kernels as a grading factor to manage DON.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".