Mass Determination of Wheat Kernels from X-ray Images
Bibliographic record
Abstract
Using soft X-ray it is possible to detect insect infestations in grain samples. Numerous studies havedetermined the potential of the soft x-ray method to identify insect-infested grain kernels by different types ofinsects. Only a few attempts have been made to determine the insect types and their developmental stages frominfested grain samples that would help to decide management strategies. Mass loss in infested grain kernelsdepends on the insect type and its life stages. Hence, a correlation between the features obtained from the x-rayimages of grain kernels and their mass may be an improved way of determining insect species and theirdevelopmental stages. In this study, area and total gray values extracted from the x-ray images of the kernels werecorrelated to their mass. Single kernels of Canada Western Red Spring wheat was scanned at 15.5 kV and 70 Awith kernels crease facing down and up or sideways. For kernels scanned with the crease down, correlationcoefficients of over 0.70 were obtained for area and total gray values. However, for kernels with crease up or on theside a poor correlation (r2 = 0.35) was determined between total gray value and mass. This was due to the fact thatreal time x-ray images have less gray values for dense areas. Hence, all images were transformed into negativeimages and then analyzed. These images showed a good correlation (r2 above 0.70) between the features from thex-ray images and kernels mass for both crease down and up kernels. This relationship may also be used to identifyforeign materials and mechanically damaged kernels in grain samples during identification of insect-infested grainkernels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".