Identification of variability in phenological responses in canola-quality Brassica juncea for utilisation in Australian breeding programs
Bibliographic record
Abstract
Canola-quality Brassica juncea is a potential alternative crop species in lower rainfall Australian environments due to its superior heat and drought tolerance, disease resistance, and pod-shatter resistance compared with the currently grown B. napus species. Canola-quality types of B. juncea that are adapted to flower and mature before water deficits and high temperatures significantly limit yield potential are currently being developed. In this study, the variability in phenological characteristics in canola-quality B. juncea is assessed, with a view to identifying the potential for including a range of genetic phenological controls on the development of the crop to assist with adaptation. Vernalisation response was compared among 17 lines of B. juncea and 3 of B. napus, using a cold treatment of 2.6°C for 25 days. Leaf number, and duration to first flower and maturity were compared in response to the vernalisation treatments. To assess day length response, the same 20 genotypes were sown at five locations with a range of sowing times, and one controlled environment, where day length was artificially extended. Development stages were assessed and the duration of particular phenological phases determined in relation to time, thermal time, and photoperiod. The major factor controlling flowering in canola-quality B. juncea genotypes was day length, with only small responses to vernalisation detected. There was sufficient variability in these traits and in thermal time to flowering under long days (intrinsic earliness) within the current canola-quality germplasm in Australia to select early flowering genotypes with potential adaptation to low-rainfall environments.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".