Conservation detection dogs: A critical review of efficacy and methodology
Bibliographic record
Abstract
1. Conservation detection dogs (CDD) use their exceptional olfactory abilities to assist a range of conservation projects. CDD are generally quicker, can cover wider areas, and find more samples than humans and other analytical tools. However, their efficacy varies between studies; methodological standardisation in the field is lacking. Considering the cost of deploying a CDD team and the limited financial resources within conservation, it is vital that their performance is quantified and reliable. This review aims to summarise what is currently known about the use of detection dogs in conservation and elucidate which factors affect efficacy. 2. We describe the efficacy of CDD across species and situational contexts like training and field work. Reported sensitivities (i.e., proportion of target samples found out of total available) ranged from 23.8% to 100% and precision rates (i.e., proportion of alerts that are true positives) from 28% to 100%. CDD are consistently shown to be better than other techniques, but performance varies substantially across the literature. There is no consistent difference in efficacy between training, testing, and field work, hence we need to understand the factors affecting this. 3. We highlight the key variables that alter CDD performance. External effects include target odour, training methods, sample management, search methodology and environment, and the CDD handler. Internal effects include dog breed, personality, diet, age, and health. Unfortunately, much of the research fails to provide adequate information on the dogs, handlers, training, experience, and samples. This results in an inability to determine precisely why an individual study has high or low efficacy. 4. It is clear that CDD can be applied to possibly limitless scenarios but moving forward researchers must provide more consistent and detailed methodologies so that comparisons can be conducted, results are more easily replicated, and progress can be made in standardising CDD work.
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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 | 0.033 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".