Space use by gray wolves (<i>Canis lupus</i>) in response to simulated howling: a case study and a call for further investigation
Bibliographic record
Abstract
Simulated wolf howling sessions are a popular ecotourism activity, but no exhaustive evaluation has been made on their potential impacts on wolf ecology. We evaluated the effects of simulated wolf howling sessions on the space use of gray wolves (Canis lupus L., 1758) in the Montmorency Forest (Quebec, Canada). Although we equipped 22 individuals with GPS collars from 2005 to 2008, only four wolves could potentially hear our 20 simulated howls (July to October 2008). We used power analyses to select two spatiotemporal scales of analysis with sufficient location data to investigate wolf reactions. We evaluated the distance and orientation of wolf movements relative to howling stations, their movement rates, and their mean distance to other collared pack members, which we used as an index of pack cohesion. We found that wolves approached howling stations (at both scales) and were closer to other pack members (at broad scale only) after simulated howls. The reactions of wolves were of relatively low magnitude, and we conclude that simulated howling sessions were unlikely to have strong negative impacts on the movement patterns of wolves. We encourage future studies to evaluate the effects of simulated howling on the activity levels and fine-scale space use by wolves.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".