Polymer seed coating of early- and late-fall-seeded herbicide-tolerant canola (<i>Brassica napus</i> L.) cultivars
Bibliographic record
Abstract
Polymer seed coatings offer an opportunity to fall seed Brassica napus earlier in October, thus avoiding difficult seeding conditions (wet/frozen soils) common in late October or early November. A multi-year field experiment was conducted at four locations in Alberta, Canada, to investigate the effect of early (mid-October) and late fall (late October-early November) canola seeding with and without a germination-inhibiting polymer seed coat. Yield and yield components were determined in glufosinate (hybrid and open-pollinated), glyphosate (open-pollinated), and imidazolinone (open-pollinated) herbicidetolerant canola cultivars. Early-seeded canola without the polymer coat had a reduced plant density of 58% compared to late-fall-seeded canola. Application of the polymer coat on early-seeded canola increased plant density by 80% compared to uncoated seed. Seed yield and dockage were not affected by seeding date when a polymer seed coating was used. Without the polymer seed coat, canola yield was reduced 42% and dockage increased 6% when seeding occurred in early vs. late fall. Increased pod production from early-fall-seeded uncoated canola compared to other seeding dates could only partially compensate for canola yield. Cultivar interactions generally were not agronomically important. The use of polymer seed coatings with all these herbicide-tolerant cultivars will allow producers to seed earlier in fall, rather than waiting for cooler soil conditions in late fall. Key words: Canola (Brassica napus L.), dormant seeding, polymer seed coating
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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".