The role of artificial pollination and pollen effect on ear development and kernel structure of different maize genotypes
Bibliographic record
Abstract
Pollen effect is important on several kernel traits in maize breeding and may vary under different pollination treatments. Our objectives in this study were i) to evaluate the effects of pollination treatments that are commonly used in maize breeding, on several ear and kernel traits, ii) to investigate if the genotypes so called “specialty corn” do have any different reaction to the pollen effect. A field trial was carried out at Dardanos Research and Application Center of Çanakkale Onsekiz Mart University, Turkey, in 2013. The experiment used a split plot design with three replicates. Four parents (three inbreds and one open pollinated landrace) were used as plant material. Three pollination treatments (open pollination, self-pollination and bulk pollination) were applied, and individual pollen effect of each parent on other parents was investigated. For this purpose, several ear and kernel traits (ear weight, kernel weight, kernel number, mean kernel weight) and biochemical features (protein, oil, carbohydrate and carotenoid content) were measured on harvested samples.The results showed that pollination treatment affected the variation on all traits except for oil content (P < 0.05). Self-pollination caused a significant reduction in kernel development. Pollen effect was found significant for most traits and this effect was evident on the related genotypes with open pollinated landrace. Results indicate that pollen effect is an important factor on kernel and ear development in small plot trials, where different types of maize are grown together.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".