Forensic application of DNA barcoding for identification of illegally traded African pangolin scales
Bibliographic record
Abstract
The escalating growth in illegal wildlife trade and anthropogenic habitat changes threaten the survival of pangolin species worldwide. All eight extant species have experienced drastic population size reductions globally with a high extinction risk in Asia. Consequently, forensic services have become critical for law enforcement, with a need for standardised and validated genetic methods for reliable identifications. The seizure of three tonnes of pangolin scales, believed to have originated from Africa, by Hong Kong Customs Authorities provided an opportunity for the application of DNA barcoding in identifying scales. Three mitochondrial DNA gene regions (COI, Cyt b, and D-loop) were amplified for a subsample of the confiscated material and compared with taxonomically verified references. All four African species were recovered as monophyletic with high interspecific uncorrected p-distance estimates (0.048-0.188) among genes. However, only three of four African species (Phataginus tricuspis, Phataginus tetradactyla, and Smutsia gigantea, originating from West and Central Africa) and one of four Asian species (Manis javanica from Southeast Asia) were identified among scales. Although the assignment of unknown scales to specific species was reliable, additional genetic tools and representative reference material are required to determine geographic origins of confiscated pangolin specimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".