Potential of rutabaga (<i>Brassica napus</i> var. <i>napobrassica</i>) gene pool for use in the breeding of <i>B. napus</i> canola
Bibliographic record
Abstract
Abstract The narrow genetic diversity in Brassica napus L. (AACC, 2n = 38) canola is one of the major impediment for continued improvement of this crop. Among the different primary gene pools of B. napus, rutabaga (B. napus var. napobrassica) is genetically distinct from spring canola. The potential value of this gene pool for use in the breeding of B. napus canola was investigated in the present study. For this, 93 advanced generation inbred lines with a spring growth habit were developed from F2 and BC1 populations of rutabaga × spring canola crosses and evaluated in replicated field trials for agronomic and seed quality traits. These inbred lines were also genotyped with SSR markers to assess the extent of allelic diversity introgressed from rutabaga into the inbred lines. Some of the inbred lines gave higher seed yield and had greater oil content than their spring canola parent. Molecular marker analysis showed that genetically distinct B. napus canola lines carrying unique alleles of the A and C genomes of rutabaga could be obtained from both F2– and BC1–derived populations. Thus, the results demonstrate the potential of using the rutabaga gene pool for the improvement of B. napus 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 | 0.000 |
| 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 | 0.001 |
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".