Potassium Fertilization Effects on Isoflavone Concentrations in Soybean [<i>Glycine max</i> (L.) Merr.]
Bibliographic record
Abstract
Soybean isoflavone concentrations vary widely, but the contribution of soil fertility and nutrient management to this variability is unknown. Field experiments from 1998 to 2000 on soils with low to high exchangeable potassium (K) concentrations evaluated K application and placement effects on isoflavone concentrations and composition of soybean in various tillage and row-width systems. Soybean seed yield and concentrations of daidzein, genistein, glycitein, leaf K, and seed K were measured. Significant increases in daidzein, genistein, and total isoflavone were observed with direct deep-banded K or residual surface-applied K on low-K soils. Positive effects of K fertilization on isoflavones were less frequent on medium- to high-testing K soils. Both individual and total isoflavones were often positively correlated with seed yield, leaf K, and seed K on low-K soils. Appropriate K management could be an effective approach to increase isoflavone concentrations for soybeans produced on low- to medium-K soils.
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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".