Invasive<i>Fallopia</i>×<i>bohemica</i>interspecific hybrids display different patterns in secondary metabolites
Bibliographic record
Abstract
Plant secondary metabolites are important traits that can benefit an invasive plant in its new environment. In the case of rapid evolution in the invaded area, the chemical weapons of introduced plants may diversify, and novel combinations or extreme concentrations of these secondary metabolites may be expressed. The invasive Fallopia species complex (F. japonica, F. sachalinensis, and the F. x bohemica interspecific hybrids) is a good model to assess how chemical traits can vary during post-introduction evolution. We analyzed and compared the composition of secondary metabolite extracts in F. x bohemica hybrids and in the parental species grown in the introduced area. HPLC-DAD profiles were obtained for each Fallopia species, and the main peaks of the HPLC chromatograms represent phenolic compounds. Analyses based on secondary metabolite profiles showed that F. x bohemica hybrids are closer to F. japonica. The F. x bohemica hybrids expressed the compound families described in F. japonica and F. sachalinensis, with quantitative variations between them. Hybrid chemical cocktails showed a diversification of chemical weapons. Furthermore, transgressive segregation was observed. Three dianthrones were identified for the first time in a Fallopia species and were more highly expressed in F. japonica and F. x bohemica hybrids. These results suggest an evolution in the chemical traits of Fallopia taxa in invaded areas such that certain genotypes may well have acquired new chemical cocktails resulting from post-introduction hybridizations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".