Molecular identification of species of <i>Physalis</i> (Solanaceae) using a candidate DNA barcode: the chloroplast <i>psbA</i>–<i>trnH</i> intergenic region
Bibliographic record
Abstract
Physalis L., an important genus of the family Solanaceae, includes many commercially important edible and medicinal species. Traditionally, species identification is based on morphological traits; however, the highly similar morphological traits among species of Physalis make this approach difficult. In this study, we evaluated the feasibility of using a popular DNA barcode, the chloroplast psbA-trnH intergenic region, in the identification of species of Physalis. Thirty-six psbA-trnH regions of species of Physalis and of the closely related plant Nicandra physalodes were analyzed. The success rates of PCR amplification and sequencing of the psbA-trnH region were 100%. MEGA V6.0 was utilized to align the psbA-trnH sequences and to compute genetic distances. The results show an apparent barcoding gap between intra- and interspecific variations. Results of both BLAST1 and nearest-distance methods prove that the psbA-trnH regions can be used to identify all species examined in the present study. In addition, phylogenetic analysis using psbA-trnH data revealed a distinct boundary between species. It also confirmed the relationship between species of Physalis and closely related species, as established by previous studies. In conclusion, the psbA-trnH intergenic region can be used as an efficient DNA barcode for the identification of species of Physalis.
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 | 0.000 |
| 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".