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 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) | 0.000 | 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 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".