Seedling age and quality upon transplanting affect seed yield of canola (<i>Brassica napus</i> L.)
Bibliographic record
Abstract
Ren, Y., Zhu, J., Hussain, N., Ma, S., Ye, G., Zhang, D. and Hua, S. 2014. Seedling age and quality upon transplanting affect seed yield of canola (Brassica napus L.). Can. J. Plant Sci. 94: 1461–1469. Seedling quality is an essential indicator for seed yield in canola, which is affected by many factors, including seedling age. Two field experiments were conducted to compare canola seedling quality and seed yield on 30-, 35-, 40-, 45-, 50-, 55-, and 60-d-old seedlings in 2011 and 2012. The relationship between seedling quality traits and seed yield of different seedling ages was also analyzed. Results revealed that the highest seed yield obtained from 40-d-old seedlings was attributed to more branches and siliques per plant. The negative effect of young seedlings (30-d-old) on seed yield was greater than that of old seedlings (60-d-old). The reduction rates in seed yield on the 30- and 60-d-old seedlings were 25.7 and 18.2%, respectively, compared with the 40-d-old seedlings. Increased root neck diameter, green leaf number, shoot, and root dry matter was the case on 40-d-old seedling transplanted plants compared with other ages. However, the increase was larger in the old seedlings than in the young seedlings. On average, the shoot and root dry weights of the 30-d-old seedlings were 1.9 and 1.7% of those in the 60-d-old seedlings. However, correlation analysis revealed that the seedlings with the highest shoot and root dry matter did not necessarily obtain the highest seed yield. Factor analysis suggested that the effects of root neck diameter and green leaf number on seed yield were more pronounced than those of shoot and root dry matter. Therefore, high seed yield in canola could be defined in terms of optimum green leaf numbers and branches per plant.
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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 | 0.001 |
| 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".