Degree of invasion of Canada goldenrod ( <i>Solidago canadensis</i> L.) plays an important role in the variation of plant taxonomic diversity and community stability in eastern China
Bibliographic record
Abstract
Abstract Understanding the impacts of invaders on plant taxonomic diversity and community stability is significant for understanding the mechanisms underlying successful invasion. This study explored the impacts of the invasive plant Canada goldenrod ( Solidago canadensis L.; goldenrod hereafter) with different degrees of invasion on plant taxonomic diversity and community stability by conducting a comparative study in eastern China. Degree of invasion was divided into the following categories, low (<35%, LDI), moderate (35–75%, MDI), and high (>75%, HDI), on the basis of the relative abundance of goldenrod in the invaded plant communities. Plant diversity, dominance, richness, and plant community stability noticeably decreased under HDI but plant diversity and dominance dramatically increased under LDI compared with the adjacent uninvaded plant communities. Plant diversity, dominance, richness, and community stability markedly declined as the degree of goldenrod invasion increased in the invaded plant communities. The greater plant diversity and dominance observed under LDI may be primarily driven by the passenger effects rather than a driving force of the presence of goldenrod. The greater competitive superiority of goldenrod over coexisting native plants under HDI might allow for invasion and enhance the risk of stochastic local extinction of several native species. A significantly positive diversity‐stability relationship was observed, which may explain the underlying mechanisms for the decreased plant community stability and drastic decline in plant diversity under HDI. Accordingly, the degree of invasion of goldenrod plays an important role in the variation of plant taxonomic diversity and community stability in eastern China.
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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 | 0.001 | 0.001 |
| 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".