Ten years of barcoding at the African Centre for DNA Barcoding
Bibliographic record
Abstract
The African Centre for DNA Barcoding (ACDB) was established in 2005 as part of a global initiative to accurately and rapidly survey biodiversity using short DNA sequences. The mitochondrial cytochrome c oxidase 1 gene (CO1) was rapidly adopted as the de facto barcode for animals. Following the evaluation of several candidate loci for plants, the Plant Working Group of the Consortium for the Barcoding of Life in 2009 recommended that two plastid genes, rbcLa and matK, be adopted as core DNA barcodes for terrestrial plants. To date, numerous studies continue to test the discriminatory power of these markers across various plant lineages. Over the past decade, we at the ACDB have used these core DNA barcodes to generate a barcode library for southern Africa. To date, the ACDB has contributed more than 21 000 plant barcodes and over 3000 CO1 barcodes for animals to the Barcode of Life Database (BOLD). Building upon this effort, we at the ACDB have addressed questions related to community assembly, biogeography, phylogenetic diversification, and invasion biology. Collectively, our work demonstrates the diverse applications of DNA barcoding in ecology, systematics, evolutionary biology, and conservation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".