Differentiation of Lingxiaohua and Yangjinhua by chloroplast genome sequencing and DNA barcoding markers
Bibliographic record
Abstract
Lingxiaohua (Campsis Flos, Campsis grandiflora (Thunb.) K. Schum) is a medicinal herb used for promoting diuresis and treating blood-related disorders by the promotion of blood circulation. It also possesses anti-inflammatory and antioxidative properties. This non-poisonous plant is frequently confused with poisonous Yangjinhua (Daturae Metelis Flos, Datura metel Linnaeus) in the market, resulting in serious anticholinergic poisoning. The confusion of these two herbs is due to the similarity in their appearances. In our study, we compared the complete chloroplast genomes of the two plants and found that they are very different in terms of their gene content and gene arrangement. There were also significant differences in the number and repeating motifs of microsatellites and complex repeats. We used universal primers for the amplification of rbcL, matK, psbA-trnH, and ITS2 regions and successfully differentiated the two plants. Furthermore, we designed two pairs of primers based on the nucleotide differences in chloroplast genomes at the rps14 and rpoC1 regions to provide additional authentication markers. The universal primers and specific primers when used together can accurately discriminate Lingxiaohua and Yangjinhua.
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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".