International Barcode of Life: Focus on big biodiversity in South Africa
Bibliographic record
Abstract
Participants in the 7th International Barcode of Life Conference (Kruger National Park, South Africa, 20-24 November 2017) share the latest findings in DNA barcoding research and its increasingly diversified applications. Here, we review prevailing trends synthesized from among 429 invited and contributed abstracts, which are collated in this open-access special issue of Genome. Hosted for the first time on the African continent, the 7th Conference places special emphasis on the evolutionary origins, biogeography, and conservation of African flora and fauna. Within Africa and elsewhere, DNA barcoding and related techniques are being increasingly used for wildlife forensics and for the validation of commercial products, such as medicinal plants and seafood species. A striking trend of the conference is the dramatic rise of studies on environmental DNA (eDNA) and on diverse uses of high-throughput sequencing techniques. Emerging techniques in these areas are opening new avenues for environmental biomonitoring, managing species-at-risk and invasive species, and revealing species interaction networks in unprecedented detail. Contributors call for the development of validated community standards for high-throughput sequence data generation and analysis, to enable the full potential of these methods to be realized for understanding and managing biodiversity on a global scale.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".