Environmental and social factors influencing wolf (<i>Canis lupus</i>) howling behavior
Bibliographic record
Abstract
Abstract Animals communicate in a variety of ways and calls are used for a number of important behaviors. Temperature, wind, time of day, and human activities can affect animals’ use of calls, particularly over long distances. Effects of group size on the use of calls can be particularly influential in territorial social carnivores. Where gray wolves ( Canis lupus ) are hunted by humans, for example, howling may make it easier for hunters to locate individuals and ultimately increase mortality. We hypothesized that a suite of factors would affect wolves’ responses to simulated howling. Specifically, we predicted that howling behavior would increase with (a) group size, (b) pup age, and (c) during crepuscular time periods and howling behavior would decrease (a) where wolves were harvested and (b) when it was hot or windy. Contrary to our prediction, larger groups did not respond as quickly to simulated wolf howls as smaller groups did and minimum and maximum daily temperatures were not good predictors of wolf howling response rates. Individuals in small litters of pups may have responded more quickly to howls than those in large litters because they are eager to seek safety from and have socialization with adults returning from foraging bouts. Although harvest did not appear to affect vocal communication by wolves, group size, pup age, time of day, wind, and number of howls emitted greatly affected wolves’ behavior and responses during howling surveys. Howling responses did not change because of harvest; response rates from wolves were nearly identical with (2.2%) and without (2.3%) harvest. The year‐round benefits of long‐distance vocal communication may outweigh the costs of increased mortality arising from howling during harvest season.
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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) | 0.002 | 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 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".