Cell-free supernatant of Devosia sp. (strain SL43) mitigates the adverse effects of salt stress on soybean (Glycine max L.) seed vigor index
Bibliographic record
Abstract
Soil salinity is a major constraint for soybean production worldwide, and the exploitation of plant growth-promoting bacteria (PGPB) and their bioactive metabolite(s) can improve plant salinity tolerance. With this objective, two experiments were performed, aiming to test 4 culture media (YEM(A), TYE(A), TS(A), and LB(A)) for growing a novel Devosia sp. (strain SL43), and then evaluating cell-free supernatants (CFS) from the Devosia sp. on germination of soybean ( Glycine max L.) seeds under salinity stress. Soybean seeds were subjected to three salinity levels (0, 100, and 125 mM NaCl) and 6 levels of Devosia sp. CFS dilution (0, 1:1, 1:100, 1:250, 1:500, 1:1000). The results indicated that 125 mM NaCl concentration caused the greatest reduction in the total number of germinated seeds (15%), germination rate (43.6%), root length (55.2%), root weight (39.3%), and seed vigor (68%), and it also increased mean germination time by 71.9%. However, Devosia -CFS improved soybean germination, and the greatest effect was obtained at 1:1 dilution. Under the highest salinity level, application of CFS at 1:1 dilution increased final germination (17.6%), germination rate (18.6%), root length (162.2%), root weight (239.4%), seed vigor index (318.7%), and also shortening mean germination time by 19.2%. The results indicated that seed vigor index was positively correlated with other traits except for mean germination time. Our study suggested that the highest productivity of Devoisa sp. was obtained from the YEM medium. Results also suggested that CFS produced by the novel Devosia sp. (SL43 strain) can successfully alleviate salt stress effects on soybean seed germination and manipulating the chemical composition of the growth medium can influence the effectiveness of these bioactive metabolites.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".