Adaptation of Glycyrrhiza glabra L. to water deficiency based on carbohydrate and fatty acid quantity and quality
Bibliographic record
Abstract
Abstract Water deficit affects agricultural systems negatively globally. This research objective was to mitigate drought’s detrimental effects on plants metabolite profiling by utilizing biofertilizers and mineral nutrition. The carbohydrate content and fatty acid profile of Licorice (Glycyrrhiza glabra) were assessed under Silicon (Si) nutrition, Claroiedoglomus etunicatum inoculation (F), and drought stress (100, 80, 60, 40, and 20% of field capacity (FC)). Results showed that Si application increased total sugar content under severe drought levels (20 and 40% FC) and made it reach 12.41 and 12.63 g/100 g DW, respectively. Sucrose, as the predominant sugar of licorice, was at its highest level (13.1 g/100 g DW) in response to integrated values of F and Si (60% FC). Gas chromatography–mass spectrometry showed that the majority of fatty acid components in plants were 9-Octadecenoic acid (8.72–71.27%), 9,12-Octadecadienoic acid (0.1–56.43%), Hexadecanoic acid (12.84–30.59%), Octadecanoic acid (6.9–15.3%), Docosanoic acid (0.57–2.77%), Eicosanoic acid (1.07–2.64%), and 7-Hexadecenoic acid (0.26–2.62%). Since a lower omega6/omega3 ratio represents a healthier product, the lowest ratio (0.25%) was observed in well-watered inoculated plants. Also, severe drought-treated plants under integrated Si and F applications showed a low omega6/omega3 ratio (1.88%). In conclusion, Si and F improved synergistically the carbohydrate content and fatty acid profile in plants, despite the drought stress.
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.
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".