A role for barcoding in the study of African fish diversity and conservation
Bibliographic record
Abstract
Africa has a rich diversity of marine and freshwater fishes, but very little taxonomic expertise or funding to describe it. New approaches to using modern technology, such as DNA barcoding, can facilitate collaboration between field biologists, reference collections and sequencing facilities to speed up the process of species identification and diversity assessments, provided specimen vouchers, tissues, photographs of the specimen and DNA sequences (barcodes) are clearly linked. The FISH-BOL project in Africa aims to establish a collaborative Pan-African regional working group to facilitate barcoding of fish across the continent and the surrounding FAO marine regions. This is being established through existing African biodiversity networks and global biodiversity programmes that are already in place. Barcoding is expected to inform African fisheries management and conservation through more accurate identification of species and their different life-history stages, by speeding up biodiversity assessments. Barcoding is an important development, contributing towards an evolutionary history perspective on which to base Africa's conservation strategies.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".