Behavioural responses of Canis familiaris to different tail lengths of a remotely-controlled life-size dog replica
Bibliographic record
Abstract
Abstract The tail of dogs and allies (Canidae) is important for intraspecific communication. We used a life-sized dog model and varied the tail length and motion as an experimental method of examining effects of tail-docking on intraspecific signaling in domestic dogs, Canis familiaris. We videotaped interactions of 492 off-leash dogs and quantified size and behaviour of approaching dogs to the model's four tail conditions (short/still, short/wagging, long/still, long/wagging). Larger dogs were less cautious and more likely to approach a long/wagging tail rather than a long/still tail, but did not differ in their approach to a short/still and a short/wagging tail. Using discriminant analyses of behavioural variables, dogs responded with an elevated head and tail to a long/wagging tail model relative to the long/still tail model, but did not show any differences in response to tail motion when the model's tail was short. Our study provides evidence that a longer tail is more effective at conveying different intraspecific cues, such as those provided by tail motion, than a shorter tail and demonstrates the usefulness of robotic models when investigating complex behavioural interactions.
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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 | 0.001 |
| 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".