Mycorrhizal Inoculation Increases Growth and Induces Changes in Specific Polyphenol Levels in Olive Saplings
Bibliographic record
Abstract
This study was conducted to investigate the effect of mycorrhizal symbiosis on the levels of polyphenols in olive saplings. Rooted stem cuttings of olive cultivar, ‘Arbequina’, were inoculated with AM fungus Rhizophagus intraradices. The inoculated plants showed more robust growth after six months, and after nine months the increase in the mycorrhizal plant’s height was 146%, and the increase in number of leaves was 117% when compared to uninoculated controls. Polyphenols in the methanol extracts of leaves were separated by HPLC and the peaks identified by using commercially available standard compounds and comparing retention time and the mass obtained with the mass spectrometer. Oleuropein, which is a major component of the olive leaf polyphenols, increased in mycorrhizal plants compared to uninoculated plants by 42%, and its derivatives, oleuroside and ligstroside, increased by 68% and 48%, respectively. The highest increase was found in the levels of luteolin-7’-O-glucoside (107% increase), while its sister compound luteolin-4’-O-glucoside increased by 43%. Only verbascoside levels were lower in mycorrhizal plants versus non-mycorrhizal plants declining to below detectable limits. Thus, inoculation of olive saplings with mycorrhizal fungi produces very positive effects on the levels of olive leaf polyphenols. Higher levels polyphenols mean better quality of leaf material for use as herbal medicine.
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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".