Cost-effective scat-detection dogs: unleashing a powerful new tool for international mammalian conservation biology
Bibliographic record
Abstract
Recently, detection dogs have been utilized to collect fecal samples from cryptic and rare mammals. Despite the great promise of this technique for conservation biology, its broader application has been limited by the high cost (tens to hundreds of thousands of dollars) and logistical challenges of employing a scat-detection dog team while conducting international, collaborative research. Through an international collaboration of primatologists and the Chinese Ministry of Public Security, we trained and used a detection dog to find scat from three species of unhabituated, free-ranging primates, for less than $3,000. We collected 137 non-human primate fecal samples that we confirmed by sequencing taxonomically informative genetic markers. Our detection dog team had a 92% accuracy rate, significantly outperforming our human-only team. Our results demonstrate that detection dogs can locate fecal samples from unhabituated primates with variable diets, locomotion, and grouping patterns, despite challenging field conditions. We provide a model for in-country training, while also building local capacity for conservation and genetic monitoring. Unlike previous efforts, our approach will allow for the wide adoption of scat-detection dogs in international conservation biology.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".