The first initiative of DNA barcoding of ornamental plants from Egypt and potential applications in horticulture industry
Bibliographic record
Abstract
DNA barcoding relies on short and standardized gene regions to identify species. The agricultural and horticultural applications of barcoding such as for marketplace regulation and copyright protection remain poorly explored. This study examines the effectiveness of the standard plant barcode markers (matK and rbcL) for the identification of plant species in private and public nurseries in northern Egypt. These two markers were sequenced from 225 specimens of 161 species and 62 plant families of horticultural importance. The sequence recovery was similar for rbcL (96.4%) and matK (84%), but the number of specimens assigned correctly to the respective genera and species was lower for rbcL (75% and 29%) than matK (85% and 40%). The combination of rbcL and matK brought the number of correct generic and species assignments to 83.4% and 40%, respectively. Individually, the efficiency of both markers varied among different plant families; for example, all palm specimens (Arecaceae) were correctly assigned to species while only one individual of Asteraceae was correctly assigned to species. Further, barcodes reliably assigned ornamental horticultural and medicinal plants correctly to genus while they showed a lower or no success in assigning these plants to species and cultivars. For future, we recommend the combination of a complementary barcode (e.g. ITS or trnH-psbA) with rbcL + matK to increase the performance of taxa identification. By aiding species identification of horticultural crops and ornamental palms, the analysis of the barcode regions will have large impact on horticultural industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".