Triggers and consequences of wolf (<i>Canis lupus</i>) howling in Yellowstone National Park and connection to communication theory
Bibliographic record
Abstract
Animal vocal communication is rife with concepts that, while important, are difficult to evaluate in nature. Particularly interesting is their application to large social mammalian carnivores characterized by year-long, loud vocalization. Here, we quantified triggers and consequences of 504 wolf ( Canis lupus Linnaeus, 1758) howl events in Yellowstone National Park observed across 16 years. We related our results to two general theories of animal communication: that vocalization is more about communicating emotional/motivational states than a purposeful transfer of detailed information and that flexibility in use of long-distance vocalizations has been important to overall behavioural plasticity and advanced sociality in non-human primates and large social carnivores. In our study, half the howl events were triggered by 12 different environmental or social situations, most of which generated levels of anxiety. The remainder were non-triggered, apparently motivated internally but in contexts that reflected basic adaptive drives such as bonding and pack coordination. Approximately half of all howl events elicited either a change in sender activity or responding howls or travel from distant wolves, which we quantified. Wolf howling was inconsistent (low percentage of occurrence) in most behavioural contexts, hence demonstrating flexibility and social discrimination in its use. Thus, both theories were strongly supported.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".