Canola growth, production and quality are influenced by seed size and seeding rate
Bibliographic record
Abstract
Canola (Brassica napus L.) is the most widespread profitable cash crop in Canada. In 2014 and 2015, direct-seeded experiments were conducted in 16 western Canada environments. “Small” canola seed (average 3.32–3.44 g 1000−1) was compared to “large” canola seed (average 4.96–5.40 g 1000−1) at five seeding rates (50, 75, 100, 125 or 150 seeds m−2). Large canola seeds increased crop density and crop biomass but decreased plant mortality, days to start of flowering, days to end of flowering, days to maturity, and percent green seed. Seed size did not influence harvested seed weight, seed oil content or seed protein content. Increasing the seeding rate of small seeds improved canola yield, but the same response did not occur for large seeds. Increasing seeding rates also increased crop density, plant mortality, crop biomass, and seed oil content, but decreased days to start of flowering, days to end of flowering, days to maturity, percent green seed, and seed protein content. Seeding rate had no impact on harvested seed weights. Because higher seeding rates often provide some of the same benefits as large seed, canola growers and the seed industry should balance seed size and seeding rate to obtain the best agronomic performance from canola.
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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) | 0.002 | 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".