Pollen number and vigor contribute to effective ovule numbers among contrasting nitrogen utilization efficiency oilseed rapes genotypes before heart stage
Bibliographic record
Abstract
Abstract Background: 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. Up to date, on study has been focused on unraveling the pre-flowering main factors to restrict EON and identifying the critical period of EON formation among contrasting nitrogen utilization efficiency (NUtE) oilseed rape genotypes.Results: In this study, we selected 18 oilseed rape genotypes with different NUtE to identify the main factors that contribute to EON, and determine if genotypes differed in the critical period of EON formation under both field and pot experiments from 2016-2018. Our results showed the high NUtE genotypes also showed 14.3% higher NUtE, accompanied with 29.4% higher yield per plant and 21.1% higher SPS. The greater productivity of the high NUtE oilseed rape genotypes was associated with 44.1% greater pollen number, 23.5% higher pollen vigor, and 39.3% lower ovule abortion rate, compared to the low NUtE genotypes. In addition, at the heart stage, the high NUtE genotypes displayed higher silique net photosynthetic rate, surface area, biomass, and RNA expression levels, compared to the low NUtE ones. Taken together, this study indicated the pollen number, pollen vigor and ovule abortion rate contributed to the final EON of diverse oilseed rape genotypes; the critical period of determining EON among contrasting NUtE genotypes was at the heart stage.Conclusion: Increasing pollen number and vigor, and decreasing ovule abortion rate before the heart stage should be the prerequisite for breeders to improve yield and NUtE of oilseed rape genotypes.
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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 | 0.000 | 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".