Developing an efficient DNA barcoding system to differentiate between Lilium species
Bibliographic record
Abstract
BACKGROUND: Lilium is an important ornamental bulb, possesses medicinal properties, and is also edible. Species within the Lilium genus share very similar morphology and macroscopic characteristics, thus they cannot be easily and clearly distinguished from one another. To date, no efficient species-specific markers have been developed for classifying wild lily species, which poses an issue with further characterizing its medicinal properties. RESULTS: To develop a simple and reliable identification system for Lilium, 45 representative species from 6 sections were used to develop a DNA barcoding system, which was based on DNA sequence polymorphisms. In this study, we assessed five commonly used DNA barcode candidates (ITS, rbcL, ycf1b, matK and psbA-trnH) and five novel barcode candidates obtained from highly variable chloroplast genomic regions (trnL-trnF, trnS-trnG, trnF-ndhJ, trnP-psaJ-rpI33 and psbB-psbH). We showed that a set of three novel DNA barcodes (ITS + trnP-psaJ-rpI33 + psbB-psbH) could be efficiently used as a genetic marker to distinguish between lily species, as assessed by methods including DNAsp, BI and ML tree, and Pair Wise Group (PWG). CONCLUSIONS: A rapid and reliable DNA barcoding method was developed for all 45 wild Lilium species by using ITS, trnP-psaJ-rpI33, and psbB-psbH as DNA barcoding markers. The method can be used in the classification of wild Lilium species, especially endangered species, and also provides an effective method for selective lily breeding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".