The effectivity of rbcL marker to identify invasive alien plant species
Bibliographic record
Abstract
Identifying and managing pathways for the introduction of alien species is one of the Kunming-Montreal Global Biodiversity Framework targets. The alternative approach to identifying the invasive alien plant species (IAPS) can be conducted using the DNA barcoding method. However, not all markers are effective in identifying the specific species. Hence, finding the right and effective marker to identify the specific species for correct identification is critical. This study examined the effectivity of ribulose 1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) marker to identify IAPS. Several primer sequences of rbcL were retrieved from the literature and then analyzed in silico by aligning the primer to the whole chloroplast DNA sequence of the target species. A selected pair of primer sequences was then employed as a marker to identify five IAPS. The sequencing results were aligned to the reference database obtained from NCBI by using BLAST. The results showed that all the species were success to be amplified using rbcL gene. The sequence results showed the query cover and per identity 99-100% of the target species. This result suggests that rbcL markers could precisely identify the IAPS target. Therefore, further research on protocol development and extensive use of DNA barcoding in IAPS identification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".