A bimodal model for oat kernel size distributions
Bibliographic record
Abstract
Oat kernel size distributions are important to the oat milling industry because size separation of kernels is routine in oat milling. Dehuller rotor speeds are set in order to deliver the optimal mechanical stress to different kernel size streams for dehulling. In this study, size distributions were evaluated by digital image analysis in 10 cultivars grown in eight environments. Observed distributions were compared with quality characteristics and with panicle characteristics and spikelet type frequencies. Size distributions within samples, as evaluated from individual kernel image areas, tended to depart from normal distributions and graphical depictions of data frequently resembled bimodal populations. A statistical test to compare a bimodal distribution with a normal distribution indicated that a bimodal model was more effective at describing the distributions. Panicle analysis indicated that two-kernel spikelets were the most abundant spikelet type found. Because two-kernel spikelets consist of one larger kernel and one smaller kernel, it is likely that the root of the bimodal distribution can be attributed to these spikelets. Although some departures from the mixture of two normal distributions can be attributed to the occurrence of one- and three-kernel spikelets, many of these departures must be attributed to other sources of variation in oat kernel size. Key words: Oats, panicle, kernel size, spikelets
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".