Identifying commercially relevant<i>Echinacea</i>species by AFLP molecular markers
Bibliographic record
Abstract
The rising interest in medicinal plants has brought several species of the genus Echinacea to the attention of many scientists. Echinacea angustifolia, E. pallida, and E. purpurea are the most important for their immunological properties, well known and widely used by the native Americans. The three species are easily distinguishable on the basis of their morphological characteristics, but it would be difficult, if not impossible, to distinguish them in commercial preparations of ground, dry plant parts of E. purpurea (the most valuable species for chemotherapeutic properties) mixed with the other two species. Species-specific molecular markers could be useful to address this issue. In the present work, using fresh material collected from cultivated Echinacea spp., AFLP analysis was used to discriminate the three species and to detect species-specific DNA fragments. By using 14 primer combinations it was possible to detect a total of 994 fragments, of which 565 were polymorphic. Overall, 89 fragments were unique to E. purpurea, 32 to E. angustifolia, and 26 to E. pallida. E+CAC/M+AAT or E+CAC/M+AGC alone provided 13, 9, and 4 or 7, 5, and 5 specific fragments for E. purpurea, E. angustifolia, and E. pallida, respectively. A validation trial to confirm the results was carried out on bulked samples of 23 accessions covering most of the genetic diversity of the three species. The results are discussed in terms of practical applications in the field of popular medicine, detecting frauds, and implications for the genus Echinacea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".