Groat proportion in oats as measured by different methods: Analysis of oats resistant to dehulling and sources of error in mechanical dehulling
Bibliographic record
Abstract
Groat proportion is the groat yield from an oat dehulling process. We compared hand, impact and compressed-air dehulling to measure groat proportion, and evaluated sources of error. Hand dehulling was the simplest and most accurate method, because all groats and hulls can be accounted for. Mechanical methods dehulled most, but not all, oat kernels. Failure to account for oats resistant to dehulling in calculations resulted in gross errors. Oats resistant to impact dehulling did not differ in groat proportion from the general population, but differed in many physical properties. Hull structure may account the most for their resistance to dehulling. Mechanically dehulled oats consistently yielded lower groat proportions than those from hand dehulling. Since the difference cannot be attributed to oats resistant to dehulling, groats must be lost during the aspiration process, common to all mechanical methods. Uniform aspiration protocols should provide a uniform error. All groat proportion values obtained here were highly correlated among themselves, except when values were not corrected for oats resistant to dehulling. A theoretical groat proportion calculated from the ratio of the mean groat mass (collected by any means available) and the mean kernel mass yielded a groat proportion value that did not differ significantly from the hand dehulling value.Key words: Oat milling, groat proportion, oat dehulling
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".