The complete chloroplast genome of <i>Persicaria perfoliata</i> and comparative analysis with four medicinal plants of Polygonaceae
Bibliographic record
Abstract
Polygonaceae is a large family of medicinal herbs that includes many species used as traditional Chinese medicine, such as Per sicaria per foliata. Here, we sequenced the complete chloroplast genome of P. per foliata using Illumina sequencing technology with the purpose of providing a method to facilitate accurate identification. After being annotated, the complete chloroplast genome of P. per foliata was compared with those of Fagopyrum tataricum, Per sicaria chinensis, Fagopyrum dibotrys, and Fallopia multiflora. The complete chloroplast genome of P. per foliata is 160 730 bp in length, containing a small single-copy region of 12 927 bp, a large single-copy region of 85 433 bp, and a pair of inverted repeat regions of 62 370 bp. A total of 131 genes were annotated, including 8 rRNA genes, 34 tRNA genes, and 84 protein-coding genes. Forty-two simple sequence repeats and 55 repeat sequences were identified. Mutational hotspot analyses indicated that five genes ( matK, ndhF, ccsA, cemA, and rpl20) could be selected as candidates for molecular markers. Moreover, phylogenetic analysis showed that all the Polygonaceae species formed a monophyletic clade, and P. per foliata showed the closest relationship with P. chinense. The study provides valuable molecular information to accurately identify P. per foliata and assist in its development and application.
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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 | 0.001 |
| 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".