Improved sampling and detection of mycotoxins in maize (Zea mays L.)
Bibliographic record
Abstract
In 2018, Ontario experienced the worst epidemic of gibberella ear rot (GER) in maize (Zea mays L.), which produced high concentrations of the mycotoxin deoxynivalenol (DON), a grading factor in Ontario. Testing bulk loads for DON has been highly variable and a great challenge for the entire industry. The primary objective was to determine the sources of this variability and to reduce them to improve testing accuracy. DON concentrations from bulk loads extracted with the traditional pneumatic probe were highly similar (R2=0.81) to the near-actual DON concentration of the bulk load. Grinding the entire 2-kg sample significantly reduced the variability compared to the traditional protocol of sub-sampling whole kernels. The comparison of lateral flow devices (LFDs) Reveal® Q+, Rida®Quick DON, QuickTox™ and ELISA to LC-MS/MS found that some highly cross-reacted with DON derivatives. The greatest source of variability was from subsampling the 2-kg probed sample and not the probe sampling itself.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".