Factors affecting oat haploid production following oat × maize hybridization
Bibliographic record
Abstract
Abstract Doubled haploids (DHs) are becoming increasingly important in crop breeding programmes but methods for producing oat DHs remain inefficient. In this study haploid and DH oat plants were produced using the oat × maize hybridization method. Factors influencing the rate of caryopsis and haploid embryo production including genotype, post‐pollination plant growth regulator application and temperature were investigated. The four growth regulators tested showed significant differences in their capacity to induce caryopsis formation with dicamba producing the highest numbers of caryopses, followed by picloram, 2,4‐dichlorophenoxyacetic acid (2,4‐D) and gibberellic acid (GA 3 ). No significant differences were observed between these growth regulators for their effect on embryo production. The concentration of dicamba was also important and was found to influence caryopsis but not embryo production, with 50 and 100 mg/l dicamba producing significantly more caryopses than 25 or 5 mg/l. Temperature had a significant impact on both caryopsis and embryo production with the magnitude and direction of response depending on genotype. Rates of haploid embryo production observed were between 0.8% and 6.7% of the pollinated florets. The proportion of haploids, which survived and were successfully doubled with colchicine following transfer to soil was between 72% and 81%.
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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 | 0.000 |
| 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".