Benefits of Cross-Pollination in Vegetable Soybean Edamame
Bibliographic record
Abstract
Dependence on cross-pollination varies widely among wild and cultivated plant species. Even among crops that are less dependent on outcrossing, such as soybean (Glycine max L.), cross-pollination can improve fruit quality and commercial value. There is a growing body of literature regarding the role of insect pollination in soybean; however, there is a knowledge gap on the intersection between the reproductive system of soybean and its pollination ecology. To address this gap, we first sought to characterize the reproductive system of vegetable soybean (edamame) in terms of benefits and reliance on outcrossing using three traditional experimental pollination scenarios in field conditions: open pollination, automatic selfing (pollinator-exclusion), and hand cross-pollination (controlled crossing). We also tested whether proximity to floral supplements planted on one edge of the field affected its reproductive outputs, and surveyed the floral visitors of the crop. Overall, we found a significant increase in fruit weight among open-pollinated plants compared to those in the automatic selfing treatment, with this effect accentuated with proximity to the flower strip. Despite open pollinated flowers having 30% higher flower abortions rates compared to automatic selfing, the number of developed seeds per fruit was similar among these treatments, with open-pollination having a greater proportion of commercial grade-A fruits. Additionally, grade-A fruits in open-pollination and hand cross-pollination treatments were similar in weight, both of which were significantly heavier than those in the automatic selfing treatment. Although edamame can automatically self, our results suggest that reproductive outputs including fruit weight and number of commercial grade-A fruits are positively affected by cross-pollination and proximity to floral supplements.
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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".