Hide‐and‐sniff: can anti‐trafficking dogs detect obfuscated wildlife parts?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it