Differences in Trait Impulsivity Indicate Diversification of Dog Breeds into Working and Show Lines
Bibliographic record
Abstract
Impulsiveness describes the inability to inhibit behaviour in the presence of salient cues. Trait-level impulsivity exists on a continuum and individual differences can be adaptive in different contexts. While breed related differences in behavioural tendency in the domestic dog (Canis familiaris) are well established, the phenomenon within lines of a breed which have been selected more recently is not well studied, although it may challenge the popular notion of breed-typical behaviour. We describe differences in impulsivity between and within two dog breeds with working and show lines selected for different levels of impulsivity: Border Collies (herding work) and Labrador Retrievers (gun work). Recent show line selection might have lessened differences in impulsivity between breeds. We tested this hypothesis on a dataset of 1161 individuals assessed using a validated psychometric tool (Dog Impulsivity Assessment Scale--DIAS). Collies were more impulsive on average, consistent with the original purpose of breed selection. Regarding line, working Collies differed from working Labradors, but show lines from the two breeds were not significantly different. Altered or relaxed artificial selection for behavioural traits when appearance rather than behaviour become the primary focus for breeders may reduce average differences in impulsivity between breeds in show lines.
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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 | 0.001 |
| 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".