A tiered barcode authentication tool to differentiate medicinal Cassia species in India
Bibliographic record
Abstract
DNA barcoding is a desirable tool for medicinal product authentication. DNA barcoding is a method for species identification using short DNA sequences that are conserved within species, but variable between species. Unlike animals, there is no single universal DNA barcode locus for plants. Coding markers, matK and rbcL, and noncoding markers, trnH-psbA (chloroplast) and ITS2 (nuclear), have been reported to be suitable for the DNA barcoding of plants with varying degree of success. Sixty-four accessions from 20 species of the medicinal plant Cassia were collected, and analyzed for these 4 DNA barcoding markers. PCR amplification was 100% successful for all 4 markers, while intra-species divergence was 0 for all 4 Cassia species in which multiple accessions were studied. Assuming 1.0% divergence as the minimum requirement for discriminating 2 species, the 4 markers could only differentiate 15 to 65% of the species studied when used separately. Adding indels to the divergence increased the percentage of species discrimination by trnH-psbA to 90%. In 2-locus barcoding, while matK+rbcL (which is recommended by Consortium for the Barcoding of Life) discriminated 90% of the species, the other combinations of matK+ITS and rbcL+trnH-psbA showed 100% species discrimination. However, matK is plagued with primer issues. The combination of rbcL+trnH-psbA provided the most accurate (100% species ID) and efficient tiered DNA barcoding tool for the authentication of Cassia medicinal products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".