Allelopathic Effects of Caffeic Acid and Its Derivatives on Seed Germination and Growth Competitiveness of Native Plants (Lantana indica) and Invasive Plants (Solidago canadensis)
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Bibliographic record
Abstract
Allelopathy has garnered considerable attention, but the effects of different allelochemicals on invasive plants remain unclear. This study addressed the knowledge gap surrounding allelopathy and its impact on native and invasive plant species. We focused on the impact of caffeic acid and its derivatives on the growth and competitiveness of the native Lantana indica and the invasive plant Solidago canadensis. We selected three allelochemicals, caffeic acid, methyl caffeic acid, and ethyl caffeic acid, for evaluation at two concentrations (0.1 mM and 1.0 mM). Three planting methods were employed: (1) a single species of S. canadensis, (2) a single species of L. indica, and (3) a combination of S. canadensis and L. indica. In addition, a control group was also included. Results revealed that high concentrations (1 mM) of methyl caffeate (MC) and ethyl caffeate (EC) significantly reduced seed germination rate, seed germination index, and seed germination speed index of L. indica compared to a low concentration (0.1 mM). Plant height, stem diameter, biomass, and root length in the control group (CK) of S. canadensis were significantly higher than those in the treated groups. However, with increasing allelochemical concentration, L. indica’s relative competitiveness gradually decreased. These findings provide insights into the concentration-dependent effects of allelopathic compounds on the growth of L. indica and S. canadensis. By analyzing how these allelochemicals influence the growth and competitiveness of native and invasive plants, the study sheds light on the dynamics of allelochemical interactions between these species. This knowledge can be pivotal for understanding plant competition dynamics in ecosystems and could inform strategies to control invasive species or promote native plant growth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it