In silico analysis of potential loci for the identification of Vanda spp. in the Philippines
Bibliographic record
Abstract
Difficulties in identifying Vanda species are still encountered, and the ambiguity in its taxonomy is still unresolved. To date, the advancement in molecular genetics technology has given rise to the molecular method for plant identification and elucidation. One hundred twenty-five (125) gene sequences of Vanda species from the Philippines were obtained from the NCBI GenBank. Four of the 25 loci were further examined using MEGA 11 software for multiple sequence alignment, sequence analysis, and phylogenetic reconstruction. The indel-based and tree-based methods were combined to compute the species resolution. The result showed that ITS from the nuclear region obtained the highest species resolution with 66.67%. It was then followed by psbA-trnH, matK, and trnL-trnF from the chloroplast genome with a species resolution of 60%, 40%, and 30.77%, respectively. ITS and psbA-trnH satisfied the ideal length for DNA barcoding as they have 655 bp and 701 bp, respectively. The locus psbA-trnH was also considered to have a higher potential to discriminate Vanda species since only a few sequences were tested for ITS. Furthermore, ITS and trnL-trnF have the highest variable rate, which is 2.9%, while matK and psbA-trnH have 2% and 1.3%, respectively. This showed the nature of the unique sequences of various species. In this study, the indel-based method provided better results than the tree-based method. It will help support further DNA barcoding studies and strengthen the conservation and protection of Vanda spp. in the Philippines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".