Influence of <i>Rhizoglomus irregulare</i> on nutraceutical quality and regeneration of <i>Lycium barbarum</i> leaves under salt stress
Bibliographic record
Abstract
Whether arbuscular mycorrhizal fungi augment the nutraceutical quality of crops under salt stress is critical as a potential agronomic practice in salinized farmland. To evaluate the effect of Rhizoglomus irregulare on the nutraceutical quality of Lycium barbarum leaves under salt stress, we analyzed growth parameters and the rutin, polysaccharide, acidic polysaccharide, and amino acids contents of 2 harvests. Inoculation of R. irregulare significantly increased the regenerated bud number (partial eta squared (PES) = 0.577, P < 0.0001) and rutin concentration (PES = 0.544, P < 0.001) of L. barbarum leaves, with and without salt stress. The biomass of the 2nd harvest (PES = 0.355, P = 0.0091) and acidic polysaccharide (PES = 0.518, P = 0.001) of L. barbarum leaves were notably enhanced by R. irregulare under 200 mmol/L salt level. Rhizoglomus irregulare had insignificant effect on polysaccharide (PES = 0.092, P = 0.221) and amino acids levels (PES = 0.263, P = 0.130) in the leaves of L. barbarum. However, inoculation by R. irregulare decreased proline level (PES = 0.761, P = 0.001) in the leaves of L. barbarum when subjected to salt stress. Taken together, these results indicate that R. irregulare significantly improved the nutraceutical quality and facilitated the sustainable production of L. barbarum leaves exposed to salt 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".