Hide‐and‐sniff: can anti‐trafficking dogs detect obfuscated wildlife parts?
Bibliographic record
Abstract
Abstract Wildlife detection dog (WDD) programs are increasingly being developed to combat illegal wildlife trafficking. However, there is little scientific research available on how sniffer dogs perform when wildlife parts are hidden during the smuggling process, which hampers the effectiveness of WDD programs. Here, we investigate the ability of WDDs to detect wildlife parts that are hidden in legally traded goods. We employed a smell test using the two most smuggled wildlife parts worldwide: elephant ivory and pangolin scales, in combination with two obfuscation items of plant and animal origin commonly employed by smugglers. We then established the sensitivity of the dogs to the target substances. Our results showed that there was a large variation between the two dogs in their sensitivity to ivory and pangolin scales. However, both dogs were generally less sensitive to ivory compared to pangolin scales, and stronger‐smelling obfuscation items could potentially lower the sensitivity of the dogs to the wildlife parts. Our study highlights the potential of dogs to detect hidden wildlife parts, but their effectiveness may depend on other aspects such as training, personality, the health of the dog, the type of wildlife substance, and the obfuscation item used. Given the variability of our findings, WDD programs need to invest in research to optimize the number and type of dogs with the right balance of traits to successfully detect wildlife parts that could potentially be obfuscated during smuggling.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".