Genetic insights: integrating DNA barcoding with taxonomy in the study of <i>Baccaurea</i> (Phyllanthaceae)
Bibliographic record
Abstract
Traditional taxonomic revisions based on macromorphological and leaf anatomical traits may have limitations in accurately distinguishing certain species within the genus. To improve taxonomic clarity, this study applied DNA barcoding to enhance the understanding of the taxonomy and phylogeny of Baccaurea Lour., a plant genus widely utilized for food, medicine, and building materials. DNA barcode regions, including rbcL, ITS2, and trnH- psbA, were used to analyze 64 samples representing 19 Baccaurea species. Using similarity Basic Local Alignment Search Tool and phylogenetic tree inference, we determined the discriminatory efficiencies of rbcL, ITS2, trnH -psbA, and their combinations rbcL + ITS2 and rbcL + ITS2 + trnH- psbA as 21.1%, 89.5%, 87.5%, 89.5%, and 89.5%, respectively. The Neighbor-Joining tree revealed well-defined, monophyletic species clusters that largely align with phylogenetic positions based on macromorphological features. Notably, our results indicate that Baccaurea parviflora and the synonymized Baccaurea scortechinii are distinct species, recommending the re-establishment of B. scortechinii as a separate species. DNA barcoding is useful in delineating species boundaries, facilitating routine specimen identification, and flagging atypical samples for detailed examination.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".