Effect of seeding date on winter canola (<i>Brassica napus</i> L.) yield and oil quality in southern Ontario
Bibliographic record
Abstract
Winter canola or winter oilseed rape (Brassica napus L.) is not commonly grown in Canada. While winter oilseed rape is the dominant growth form in Europe, Canadian canola production is dominated by spring types in western Canada. Research conducted in the 1980s indicated that the environmental conditions in southern Ontario are well suited to the production of winter canola. Since then, however, interest in the crop has ebbed, and little to no research has been conducted on the agronomic issues that potentially limit its adoption in the province. The objective of this research was to identify an optimal seeding date for winter canola in southern Ontario. Three winter canola hybrids were evaluated across five seeding dates, ranging from early September to late October. The results established the first two weeks of September as the optimal seeding period for winter canola in southern Ontario. Seeding winter canola during this period, such that greater than 600 growing degree days could be accumulated before the first fall frost, not only reduced winterkill to approximately 20%, but also maximized yield potential and ensured optimal oil quality. Winter canola showed great potential for production in southern Ontario, and its addition to current crop rotations would diversify and enhance crop production practices in this portion of the province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".