Oat quality and yield as affected by kernel moisture at swathing
Bibliographic record
Abstract
Increased demand for high-quality oat has rekindled interest in improving oat (Avena sativa L.) yield and quality by managing the timing of oat swathing. The objective of this study was to determine the effect of swathing at five levels of kernel moisture on yield and seed quality at Indian Head, SK, and Brandon, MB, from 1997 (Indian Head only) to 2000. The largest benefit to yield and quality was achieved when swathing was delayed from 50 to 41% kernel moisture. Kernel weight, plump seed and groat yield were optimized when kernel moisture was between 36 and 30% moisture content. Dockage, protein content and oil content also improved with later swathing dates. Swathing should not begin until a kernel moisture content of 41% has been reached. After 30% kernel moisture was reached, no improvement in oat yield and quality occurred when swathing was delayed any further. Key words: Avena sativa L., test weight, thin seed, groat yield, protein, germination
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".