Yield and quality of canola seed as affected by stage of maturity at swathing
Bibliographic record
Abstract
Swathing is an important canola (Brassica napus L.) harvest operation in western Canada. The determination of the optimum timing for this operation is worth considering, as premature swathing may lead to reduced seed yield and quality. Seed yield and quality of three canola cultivars (44A89, AC Excel and Ebony), as affected by two seeding dates and several harvest times (six or eight swathing times and one direct combined treatment) was investigated on a Black Chernozem silty loam soil at Melfort, Saskatchewan, Canada, during 1998, 2000 and 2001. Seed yield, weight, protein content (oil-free meal basis) and oil content generally increased with seed development and swathing time. Early seeding was more conducive to achieving higher seed yield, especially in good growing conditions, and resulted in heavier mature seeds with higher oil content. Seed oil composition also changed during seed development. The proportion of oleic (C18:1) and linolenic (C18:3) acids increased, while that of myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), linoleic (C18:2) and ara chidic (C20:0) acids decreased. The levels of the long chain fatty acids eicosenoic (C20:1) and erucic (C22:1) acids were unaffected. However, the overall amount of fatty acids synthesized (mg 100 seeds-1) increased as seeds matured. Swathing was advantageous over direct combining in preventing weather-induced shattering. Key words: Brassica napus, canola, fatty acid, oil, protein, seed development, seed quality, shattering, direct combining, swathing
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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".