Sorting capability and grain recovery of deoxynivalenol contaminated wheat is affected by calibration and vitreous kernel settings from near-infrared transmittance technology
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Bibliographic record
Abstract
Deoxynivalenol (DON) is a toxic secondary metabolite in wheat which affects animal performance. Limited post-harvest sorting technologies are available to remove infected kernels thereby allowing safe use in livestock. A technology was developed which uses near-infrared spectrometry combined with a seed singulation sorter by BoMill AB (Sweden) which is purported by the manufacturer to remove Fusarium infected grain. The objective of this study was to determine if Fusarium infected grain could be removed using the BoMill equipped with the Fusarium calibration resulting in grain with less than 5,000 μg/kg DON, and therefore legal to feed to poultry and beef in Canada. The secondary objective was to determine the optimal HVK settings within the two calibrations to determine if sorting based on Fusarium damage is more effective than sorting based on protein content. The settings tested were HVK, HHVK, and HHHVK. The HVK settings are reported by the manufacturer to be related to the relative opacity from the starch granules. Using the HHVK setting in the Fusarium calibration resulted in highest recovery (50.3% vs HVK 40.8% and HHHVK 45.1%) and intermediate levels of DON (1,800 μg/kg vs HVK 1,600 μg/kg and HHHVK 2,400 μg/kg), and intermediate rejection rates (29.0% vs HVK 38.7% and HHHVK 22.7%). When using the protein calibration with HHVK setting, the recoveries were similar to the Fusarium calibration (51%), the rejection rates were lower (17.5%), but DON concentration was higher (2,900 μg/kg). Sorting of pooled samples was effective, however additional sieving was required to separate grain of like sizes for optimal function. BoMill sorting using the Fusarium calibration and HHVK setting will effectively sort high DON wheat. The Fusarium calibration was superior to the protein calibration as it resulted in similar recovery but lower DON concentrations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it