Pollen Grain Number and Viability Contribute to Variation in Effective Ovule Number Among Oilseed Rape Genotypes
Bibliographic record
Abstract
Abstract While no significant differences in initial ovule number were found among oilseed rape genotypes, there was a large variation in effective ovule number (EON), which determines the final seeds per silique (SPS), a critical component of yield. In this study, we selected 18 oilseed rape genotypes with contrasting nitrogen utilization efficiency (NUtE) to unravel the main factors responsible for different EON and determine the critical period of EON formation under both a field and a pot experiments from 2016-2018. The high NUtE genotypes displayed significantly higher NUtE by 14.3%, along with greater yield per plant (29.4%) and SPS (21.1%) than the low NUtE genotypes. The greater productivity of the high NUtE genotypes was associated with 44.1% higher pollen grain number, 23.5% greater pollen viability, and 39.3% lower ovule abortion rate, compared to the low NUtE genotypes. In addition, the heart stage was the critical ovule development period for delineating the variability of EON among contrasting NUtE oilseed rape genotypes, when the high NUtE genotypes displayed higher silique net photosynthetic rate, surface area, biomass, and RNA expression levels. Taken together, this study indicated the pollen grain number, pollen viability and ovule abortion rate contributed to the final variation in EON and the heart stage was the critical period of determining the EON differences among contrasting NUtE genotypes. Increasing pollen grain number and pollen viability, and decreasing ovule abortion rate before heart stage should be the prerequisite for breeders to improve yield and NUtE of oilseed rape genotypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".