Heavy metal pollution improves allelopathic effects of Canada goldenrod on lettuce germination
Bibliographic record
Abstract
Abstract Large amounts of heavy metals have been released into the environment. Thus, the allelopathic effects of invasive alien species on the germination performance of co-occurring indigenous species may be altered or even heightened with the rapid growth in heavy metal pollution. This study evaluated the impacts of Canada goldenrod (Solidago canadensis L.) leaf extracts at concentrations of 0, 10 or 20 gl 1 on the germination of lettuce under different forms of heavy metal pollution (Cu2+, Pb2+ or a combination of Cu2+ and Pb2+; 35 mgl 1) during incubation in Petri dishes for 10 days. Goldenrod leaf extracts (high concentration) reduced growth of aboveground and belowground parts of lettuce as well as competition for light and soil nutrients. However, low concentrations of goldenrod leaf extracts dramatically improved growth of lettuce roots, competition for light, soil nutrient availability, leaf photosynthetic area and growth competitiveness. The combination of goldenrod leaf extracts and heavy metal pollution was synergistic on most lettuce germination parameters, probably because high concentrations of goldenrod leaf extracts together with heavy metal pollution had a synergistic negative impact on lettuce germination. Consequently, increased levels of heavy metal pollution may favour invasion of invasive alien species while largely suppressing germination of indigenous species.
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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) | 0.001 | 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".