Trends in DNA barcoding and metabarcoding
Bibliographic record
Abstract
This open-access special issue features 12 full articles representing emerging trends from the international DNAbarcoding community. Several articles highlight how DNA-based techniques are elucidating the species diversity,biogeography, and conservation status of Africas biodiversity. Another prominent theme is the movementtowards big biodiversity data using high-throughput, individual-based DNA barcoding methods, which preservevoucher specimens and abundance data, as well as bulk sample-based metabarcoding. Methodological developments are enhancing the detection of specific species and whole communities using environmental DNA(eDNA) barcoding and metabarcoding. Data are also expanding in terms of genetic coverage; in this issue, a newdatabase is established for a secondary fungalDNAbarcode marker, and multi-kingdom, multi-marker biodiversitysurveys are gaining traction. DNA barcode sequence data, often combined with complementary markers or taxonomic information, are increasingly contributing to large-scale phylogenetic projects, with implications for understanding evolutionary history, community structure, and conservation priorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".