Effect of Canine Oxytocin Receptor Gene Polymorphism on the Successful Training of Drug Detection Dogs
Bibliographic record
Abstract
Drug detection dogs can be trained to locate various prohibited drugs with targeted odors, and they play an important role in the interdiction of drug smuggling in human society. Recent studies provide the interesting hypothesis that the oxytocin system serves as a biological basis for co-evolution between dogs and humans. Here, we offer the new possibility that genetic variation of the canine oxytocin receptor (OXTR) gene may regulate the success of a dog's training to become a drug detection dog. A total of 340 Labrador Retriever dogs that were trained to be drug detection dogs in Japan were analyzed. We genotyped an exonic SNP (rs8679682) in the OXTR gene and compared the training success rate of dogs with different genotypes. We also asked dog trainers in the training facility to evaluate subjective personality assessment scores for each dog and examined how each dog's training success was related to those scores. A significant effect of the OXTR genotype on the success of the dogs' training was found, with a higher proportion of dogs carrying the C allele (T/C and C/C genotypes) being successful candidates than dogs carrying the T/T genotype. Dog personality scores of Training Focus (Factor 1) were positively correlated with an increased likelihood that a dog would successfully complete training. Although the molecular mechanism of the OXTR gene and its functional pathway related to dog behavior remains unknown, our findings suggest that canine OXTR gene variants may regulate individual differences between dogs in their responsiveness to training for drug detection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".