Tell-tale signals: faces reveal playful and aggressive mood in wolves
Bibliographic record
Abstract
Animals have evolved a wide variety of signals that punctuate social interactions, thus optimizing communication systems. In the study of communicative strategies, real and play fighting are good models, as they are associated with risks and injuries. Therefore, within these two domains, clear ‘statement’ signals should be recruited to disambiguate messages. We gathered video data (135 h) from 38 wolves from three mixed-age captive groups ( Canis lupus arctos, C. l. lupus, C. l. occidentalis ) and analysed all the facial expressions in aggressive and playful domains. The analyses revealed the presence of three different threatening faces (Light-, Medium- and High-TF), mainly performed during aggressive encounters, which differed in the degree of mouth opening and lip stretching. We also identified two different relaxed open mouth facial expressions (Full- and Half-ROM) exclusively performed during play and possibly signalling different levels of playful arousal. Interestingly, facial expressions did not differ between groups thus suggesting a hard-wired facial communication system at least in these two domains. The next step will be to test hypotheses on the efficacy of such facial displays in eliciting an appropriate response in the receivers, potentially translating into a fine modulation of behavioural patterns in both play and real fighting. • Wolf facial displays differ strongly in playful and aggressive contexts. • During aggression three different threatening faces can be present. • Wolves display two different playful facial displays. • Playful and aggressive facial displays always differ in the same key elements. • Play and aggressive facial displays were consistent across three wolf groups.
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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 | 0.000 | 0.000 |
| 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".