Plant growth-promoting bacteria and silicon fertilizer enhance plant growth and salinity tolerance in<i>Coriandrum sativum</i>
Bibliographic record
Abstract
Plant growth-promoting bacteria (PGPB) and silicon (Si) can augment salinity tolerance in plants. In this study, 25 potential PGPB were isolated from alfalfa rhizosphere and screened for their ability to synthesize indole-3-acetic acid, 1-aminocyclopropane-1-carboxylate deaminase, and solubilize tricalcium phosphate. Two promising strains were tentatively identified as Pseudomonas pseudoalcaligenes (KB-10) and P. putida (KB-25) based on phenotypic, biochemical and 16S rRNA gene phylogeny. Subsequently, a pot experiment was conducted to evaluate the effectiveness of KB-10 and KB-25 treatment, alone or in combination with Si fertilizer, in alleviating salinity stress in coriander. The results showed that treatment with PGPB strains and/or Si significantly increased relative water content, concentrations of photosynthetic pigments, peroxidase activity, total biomass, salt tolerance index, and reduced salt-induced total phenolic contents. Overall data suggested that the combined application of PGPB and Si fertilizer could be a feasible and effective approach to improve growth and salinity tolerance in coriander.
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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) | 0.001 | 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".