Allelopathy of Aromatic Species on the Germination of Cereus jamacaru DC. subsp. jamacaru (Cactaceae)
Bibliographic record
Abstract
Cereus jamacaru DC subsp. jamacaru, has been suffering from severe anthropic pressure, in addition, when their seeds are dispersed, some end up not germinating due to the action of allelochemicals. Therefore, the present study was to evaluate the allelopathic effect of the essential oil (EO) from four species over C. jamacaru germination, as well as to identify their constituents. Four plants were selected for EO extraction (Mesospherum suaveolens (L.) Kuntze, Lantana montevidensis (Spreng.) Briq., Lantana camara L., and Tarenaya spinosa (Jacq.) Raf.) and the chemical analysis was performed by GC-MS. In order to evaluate the allelopathic activity of the EO’s, the C. jamacaru seeds were treated with the EO’s. The results showed that the EO’s presented heterogeneity in their composition, with M. suaveolens presenting the highest number of constituents (44), followed by L. camara (26), T. spinosa (23) and L. montevidensis (22). All the oils negatively affected the C. jamacaru germination percentage in a concentration-dependent manner. Regarding the GVI, the M. suaveolens, L. montevidensis and L. camara OEs significantly decreased this index at all analyzed concentrations. Based on the results obtained, it is suggested that C. jamacaru should not be sown close to the aforementioned aromatic species in reforestation programs.
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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) | 0.001 | 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".