Genetic dissection of behavioral traits related to successful training in drug-detection dogs
Bibliographic record
Abstract
Abstract Drug detection dogs play integral roles in society; however, the interplay between their behaviors and genetic characteristics remains uninvestigated. To profile the genetic traits associated with various behaviors related to the successful training of drug detection dogs, we collected and analyzed more than 230,000 genetic variants from 326 dogs belonging to the German Shepherd or Labrador Retriever breeds. Behavioral breed differences were observed in ‘friendliness to humans’ and ‘tolerance to dogs’. A strong positive correlation was observed in the genomic heritability of behavioral traits between breeds (Pearson’s r = 0.964, P = 0.008), indicating a similar degree of genetic influence on the behavioral traits between breeds. A genome-wide association study identified 18 single nucleotide polymorphisms potentially associated with drug detection abilities and three behavioral traits (interest in the dummy, tolerance to dogs, and friendliness to humans) related to drug detection abilities. Among them, 61 protein coding genes, including those associated with anxiety-related or exploration behavior in mice, such as Atat1 and Pfn2, were located surrounding the candidate polymorphisms. These findings highlight genetic characteristics associated with behavioral traits that are important for the successful training of drug detection dogs, which might support improved breeding and training of these dogs.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".