TigerBase: A DNA registration system to enhance enforcement and compliance testing of captive tiger facilities
Bibliographic record
Abstract
The illegal trade in tigers ( Panthera tigris ) and their derivatives, such as bones, teeth and pelts, is a major threat to the species' long-term persistence. As wild tiger populations have dwindled, a large proportion of trafficked tiger products now derive from captive breeding facilities found throughout Asia. Moreover, wild tigers have been poached and laundered into captive facilities, then falsely designated as captive-bred. The establishment of a DNA registration system is recognized as a key tool to monitor compliance of captive facilities, support tiger trade investigations and improve prosecution outcomes. Here, we present a standardised wildlife forensic DNA profiling system for captive tigers called TigerBase . TigerBase has been developed in four South-East Asia countries with captive tiger facilities: Malaysia, Vietnam, Thailand and Lao PDR. TigerBase DNA profile data is based on 60 single nucleotide polymorphism (SNP) markers, genotyped using two different TaqMan®-based approaches: OpenArray® chip (capable of genotyping 60 SNPs for 48 samples in a single chip), and singleplex TaqMan® assays (capable of genotyping one SNP for one sample per reaction). Of the 60 SNPs, 53 are autosomal nuclear markers, suitable for individualisation and parentage applications, two are sex-linked markers, suitable for sexing, and five are mtDNA markers, suitable for maternal subspecies identification. We conducted a series of validation experiments to investigate the reliability and limitations of these SNP genotyping platforms. We found that the OpenArray® chip platform is more appropriate for generating reference data given its greater throughput, while the singleplex TaqMan® assays are more appropriate for genotyping lower quality casework samples, given their higher sensitivity and throughput flexibility. Only 19 autosomal nuclear markers were validated as singleplex TaqMan® assays, which generally provides ample power for individualisation analysis (probability of identity among siblings was <6.9 ×10 −4 ), but may lack power for specific parentage questions, such as determining parentage of an offspring when one of the parent's genotypes is missing. Further, we have developed pipelines to support standardised SNP calling and decrease the chance of genotyping errors through the use of analytical workflows and synthetic positive controls. We expect the implementation of TigerBase will enhance enforcement of tiger trafficking cases and encourage compliance among captive tiger facilities, together contributing to combatting the illegal tiger trade.
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) | 0.000 | 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 teacher head, 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".