Testing plant barcoding in a sister species complex of pantropical <i>Acacia</i> (Mimosoideae, Fabaceae)
Bibliographic record
Abstract
Acacia species are quite difficult to differentiate using morphological characters. Routine identification of Acacia samples is important in order to distinguish invasive species from rare species or those of economic importance, particularly in the forest industry. The genus Acacia is quite abundant and diverse comprising approximately 1355 species, which is currently divided into three subgenera: subg. Acacia (c. 161 species), subg. Aculiferum (c. 235 species), and subg. Phyllodineae (c. 960 species). It would be prudent to utilize DNA barcoding in the accurate and efficient identification of acacias. The objective of this research is to test barcoding in discriminating multiple populations among a sister-species complex in pantropical Acacia subg. Acacia, across three continents. Based on previous research, we chose three cpDNA regions (rbcL, trnH-psbA and matK). Our results show that all three regions (rbcL, matK and trnH-psbA) can distinguish and support the newly proposed genera of Vachellia Wight & Arn. from Acacia Mill., discriminate sister species within either genera and differentiate biogeographical patterns among populations from India, Africa and Australia. A morphometric analysis confirmed the cryptic nature of these sister species and the limitations of a classification based on phenetic data. These results support the claim that DNA barcoding is a powerful tool for taxonomy and biogeography with utility for identifying cryptic species, biogeograhic patterns and resolving classifications at the rank of genera and species.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".