Inhibitory effect of rosemary essential oil, loaded in liposomes, on seed germination of <i>Acacia saligna</i>, an invasive species in Mediterranean ecosystems
Bibliographic record
Abstract
Acacia saligna (Labill.) Wendl. is native of southwestern Australia, but has been planted extensively in many areas of the world, including the Mediterranean region, becoming highly invasive especially in coastal habitats. The aim of this study was to test whether the indigenous Rosmarinus officinalis L. essential oil (EO), loaded in liposomes, can inhibit the seed germination of the invasive alien Acacia saligna. Variability in seed germination requirements and responses among populations were evaluated. Germination tests under light, at constant temperatures and three concentrations of rosemary EO were carried out. Among the examined factors, only the EO amount and temperature had a highly significant effect on seed germination. The lowest EO quantity did not show differences compared with the control, while the highest amount inhibited significantly seed germination of all populations at all the tested temperatures. No seed recovered the ability to germinate after the EO treatment with either of the two washing methods. Our results allowed us to identify the minimum amount of rosemary EO capable of inhibiting the seed germination of the invasive Acacia saligna. These results could be useful for the control of this invasive alien species, thus allowing the conservation of indigenous Mediterranean plant species and habitats.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".