Use of objective imaging systems to assess subjective grain appearance traits important to the US rice industry
Bibliographic record
Abstract
Abstract Background and objectives Milled rice grain appearance traits, including chalk, determine its grade, price, and marketability. These appearance traits are evaluated visually (VI) in the United States by officially trained inspectors and at commercial mills. Digital imaging systems (IS) provide an alternative means to objectively and rapidly measure these traits. The goals of this study were to compare chalk values determined by VI and IS and to identify IS parameters that are associated with the five grain appearance traits commonly assessed by the US rice industry. Findings Milled rice chalk determined by three VI methods used by commercial mills, a rice export company, and government inspectors, and three IS, WinSeedle, SeedCount, and S21, differed significantly. However, all six methods agreed on ranking of the best and worst of 20 US cultivars for chalkiness. Multiple linear regression analyses identified quality parameters from each IS that are associated with—bran streaks, chalk, kernel color, uniformity length, and appearance overall as determined by commercial mills. Conclusions IS can rapidly quantify rice grain appearance traits but agree with subjective ratings for chalkiness only when differences are extreme. Subjective grain appearance traits as determined by commercial mills appear to be based on several parameters detected by IS. Significance and novelty IS demonstrated that discolored kernels and grain chalkiness are the major factors explaining differences in overall appearance of US long‐grain varieties as subjectively assessed by commercial mills.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".