Potassium translocation combined with specific root uptake is responsible for the high potassium efficiency in vegetable soybean
Why this work is in the frame
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Bibliographic record
Abstract
Uptake of potassium (K) in crops depends mainly on the root system. Field, pot and hydroponic experiments were carried out to characterise root morphological traits and examine their roles in K uptake and utilisation of vegetable soybean (edamame) (Glycine max (L.) Merr.). Of 40 genotypes, two high K-efficiency (HKE) and two low K-efficiency (LKE) genotypes were identified and compared at two levels of K application: nil or 120 kg K2SO4 ha–1. HKE genotypes had shorter total root length and smaller root surface area and root volume than LKE genotypes, but responded earlier to low-K conditions by adjusting root architecture. In plants receiving nil K, total root length was increased by 10.4–21.8% for HKE genotypes but decreased by 5.5–9.5% for LKE genotypes at the V4 stage relative to plants receiving applied K. HKE genotypes were more efficient in redistributing K from source to sink tissue, especially from leaf. Of the total K in vegetative tissues, 35.0–46.4% was redistributed to seed in HKE genotypes, whereas only 19.7–28.2% was redistributed in LKE genotypes. HKE genotypes also had a higher specific K uptake rate (K uptake per unit root length), 1.6–1.7 times higher than LKE genotypes at the R5 stage. This indirectly indicated a stronger root K acquisition in HKE genotypes. This study suggests that future vegetable soybean improvement with greater K efficiency should be focused on the selection of higher K-redistribution rate and specific K-uptake rate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it