Combined effects of plant growth‐promoting rhizobacteria and genistein on nitrogen fixation in soybean at suboptimal root zone temperatures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Application of plant growth‐promoting rhizobacteria (PGPR) or the plant to bacteria signal molecule genistein has been shown to increase nodulation and nitrogen (N) fixation by soybean [Glycine max (L.) Merr.] over a range of root zone temperatures (RZTs) and, specifically, off‐sets at least some of the ill‐effects of low RZTs. Two sets of controlled‐environment experiments, one on a growth bench and the other in a greenhouse, were conducted to examine the combined ability of both PGPR and genistein to reduce the negative effects of low RZT on soybean nodulation and N fixation. Each of two the PGPR strains, Serratia proteamaculans 1–102 and Serratia liquefaciens 2–68 were co‐inoculated with Bradyrhizobium japonicum USDA 110 preincubated with 17.5 (somewhat inhibitory), and 15°C (very inhibitory). At RZTs of 25 and 17.5°C PGPR strains and genistein in combination increased the number of nodules and the amount of Nn fixed. The most stimulatory effect was observed at 17.5°C for the combination: S. proteamaculans 1–102 plus B. japonicum USDA 110 pre‐incubated in 15 μM genistein under greenhouse conditions. For most treatment combinations the stimulatory effects of PGPR and genistein were additive at RZTs of 17.5 and 25°C. Surprisingly, the combination of these two factors resulted in antagonism at the very inhibitory RZT of 15°C. The results suggest that the negative effects of certain low RZTs could be more effectively off‐set by combined treatments of PGPR plus geneistin pre‐incubation of rhizobial cultures than by their individual treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it