Mineral Nitrogen Availability and Isoflavonoid Accumulation in the Root Systems of Soybean (<i>Glycine max</i> (L.) Merr.)
Bibliographic record
Abstract
Isoflavonoids, as plant‐to‐bacteria signal molecules, play an important role in the establishment of the soybean (Glycine max (L.) Merr.)–Bradyrhizobium japonicum nitrogen (N) fixing symbiosis. They are essential to the development of effective root nodules and responsible for inducing the nod genes of B. japonicum. Because N affects a broad range of infection events, especially the symbiotic events occurring within 18 h of inoculation, it is reasonable to hypothesize that mineral N disrupts the interorganismal signal exchange between soybean host plants and B. japonicum. High performance liquid chromatographic (HPLC) analysis of root extracts of soybean, inoculated with B. japonicum or not, grown with various levels of mineral N in the rooting medium were performed to test this hypothesis. The results of these studies indicated that: (1) at early plant growth stages (before the onset of N fixation), a strong negative relationship between N application and soybean root isoflavonoid (genistein and daidzein) concentrations existed; (2) although isoflavonoid (genistein and daidzein) concentrations in both inoculated and non‐inoculated soybean root systems were generally decreased by N application, at very low N levels (10 mg N l−1) genistein in the non‐inoculated plant roots was not decreased relative to the 0 N plants; (3) averaged over all mineral N treatment levels and sampling times, inoculation of soybean with B. japonicum increased root daidzein concentrations (P > 0.05), but did not affect genistein. Overall, N application reduced the isoflavonoid concentration of soybean root systems, which probably plays a part in the regulation of soybean nodule formation by available N.
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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 | 0.001 |
| 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".