Improving salinity resilience in <i>Swertia chirayita</i> clonal line with <i>Lactobacillus plantarum</i>
Bibliographic record
Abstract
Plants defense responses to abiotic stresses, including salinity stress, involve stimulation of defense related pathways such as biosynthesis of secondary metabolites and induction of endogenous antioxidant enzyme responses. In the present study, a single seed origin clonal line of Swertia chirayita inoculated with Lactobacillus plantarum (LP) was grown under different salinity levels. Control had no LP inoculation. S. chirayita inoculated with LP showed higher accumulation of proline, low proline dehydrogenase activity, up-regulation of pentose phosphate pathway, down-regulation of succinate dehydrogenase activity (Krebs cycle) and low total phenolic content with increased salt concentrations. In comparison, S. chirayita without LP adopted a different biochemical mechanism to counter salt stress (NaCl) by up-regulating both pentose phosphate pathway and Krebs cycle along with stimulation of phenolic biosynthesis. Guaiacol peroxidase (GPX) activity increased with and without LP treatment in response to increasing concentrations of salt. These results indicate that S. chirayita inoculated with LP exhibits a greater salinity stress tolerance than S. chirayita without LP by adopting a more energy efficient defense responses and potentially efficiently partitioning carbon flux between primary and secondary metabolism to counter salt induced oxidative stress.
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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".