Validating a remote saliva collection tool for genomic analyses in free ranging dogs
Bibliographic record
Abstract
Saliva is a well-established source of DNA for various applications due to its non-invasive collection and its provision of high-quality DNA. However, its use in wild and free-ranging animal research remains limited due to challenges in collection without direct animal handling. In this study, we developed and evaluated a hands-off saliva collection method designed for free-ranging domestic dogs (FRDs), serving as a model for non-invasive genetic sampling of wildlife. Our method utilized a funnel paired with a commercially available Performagene kit (DNA Genotek, Canada), presented to the dog in the presence of an operator. The dog was free to approach and interact with the apparatus, depositing saliva while trying to reach bait. We compared DNA yield and genotyping success from samples using this hands-off method with those collected via the manufacturer's recommended method. We collected 461 saliva samples from 326 FRDs, performing 750 DNA extractions. Samples collected by hand yielded significantly higher DNA concentrations after the first extraction attempt (mean = 46.3 ng/µL) than those collected using the hands-off method (mean = 32.2 ng/µL). Despite lower DNA concentrations, genotyping success did not significantly differ between methods, demonstrating that the hands-off method can yield DNA suitable for genomic analyses. The hands-off saliva collection method is a viable alternative to invasive sampling, addressing ethical concerns and enabling genomic studies in wild animals. Furthermore, our method mitigates sampling bias toward bold individuals, a common limitation in behavioral and genetic studies of free-ranging animals. With minor adaptations, this method could be applied across various species, including more elusive ones, contributing to conservation genetics and behavioral ecology research.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".