Potential of rutabaga (<i>Brassica napus</i> var. <i>napobrassica</i>) gene pool for use in the breeding of hybrid spring <i>Brassica napus</i> canola
Bibliographic record
Abstract
Abstract The use of rutabaga for increasing the level of heterosis in spring canola was investigated. For this, test hybrids were produced by crossing four inbred populations, derived from F2 and BC1 of two rutabaga × spring canola crosses, to their spring canola parent, and were tested in field‐plots for agronomic and seed quality traits. Average mid‐parent heterosis (MPH) in these test hybrid populations was about 5%‐15% for yield. Compared to the F2‐derived population, the BC1‐derived population exhibited 1.8‐times greater MPH for yield. No correlation was found between inbred and test hybrid for yield; however, a positive correlation of genetic diversity of the inbred lines was found with MPH (r = .58) and hybrid yield (r = .36) suggesting that non‐additive effect of the genes may play an important role for high yield in hybrids. About 2% negative MPH was found for days to flowering, while almost no heterosis was found for seed oil and protein contents. Thus, our study demonstrates the potential of the rutabaga gene pool for use in hybrid canola breeding.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".