Association between coat colour and the behaviour of Australian Labrador retrievers
Bibliographic record
Abstract
Abstract Background Making assumptions regarding temperament and intelligence based on the physical appearance of dogs can be a conscious or unconscious human act. Labrador retrievers with chocolate-coloured coats are anecdotally considered to be less trainable and more hyperactive and aggressive than their black or yellow peers. To test these assertions, we analysed the owner-reported behavioural traits of Labradors in relation to both their observable coat colour, and their TYRP1 and MC1R genotypes. Results We used the results of an owner-based questionnaire to determine scores for 21 behavioural traits and test whether these scores varied with coat colour ( n = 225). Familiar dog aggression was the only trait that was found to vary significantly with coat colour ( P = 0.013). Yellow Labradors had a higher score than chocolate Labradors, even when corrected for multiple testing ( P = 0.021). We repeated the analyses for a subset of 63 Labradors with available genotyping data for the genes (MC1R and TYRP1) that are known to determine the primary coat colours in Labradors. Familiar dog aggression scores varied with both the observed coat colour and MC1R genotype. Dogs homozygous for MC1R recessive allele (with yellow coat colour) scored higher for familiar dog aggression than either black or chocolate Labradors. However, no association maintained significance when incorporating Bonferroni correction. Dog trainability scores decreased additively as the number of recessive brown alleles for TYRP1 increased. This allelic association was independent of the observable coat colour. Dogs homozygous for the brown allele were considered less trainable than dogs with no brown alleles ( P = 0.030). Conclusions Our results do not support that chocolate-coloured Labradors are more hyperactive or aggressive than either black or yellow Labradors. Trainability scores varied with TYRP1 genotype but not the observable coat colour. Further validation is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".