Faster and farther: wolf movement on linear features and implications for hunting behaviour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Predation by grey wolves Canis lupus has been identified as an important cause of boreal woodland caribou Rangifer tarandus caribou mortality, and it has been hypothesized that wolf use of human‐created linear features such as seismic lines, pipelines and roads increases movement, resulting in higher kill rates. We tested if wolves select linear features and whether movement rates increased while travelling on linear features in north‐eastern Alberta and north‐western Saskatchewan using 5‐min GPS (Global Positioning System) locations from twenty‐two wolves in six packs. Wolves selected all but two linear feature classes, with the magnitude of selection depending on feature class and season. Wolves travelled two to three times faster on linear features compared to the natural forest. Increased average daily travelling speed while on linear features and increased proportion of steps spent travelling on linear features increased net daily movement rates, suggesting that wolf use of linear features can increase their search rate. Synthesis and applications . Our findings that wolves move faster and farther on human‐created linear features can inform mitigation strategies intended to decrease predation on woodland caribou, a threatened species. Of the features that can realistically be restored, mitigation strategies such as silviculture and linear deactivation (i.e. tree‐felling and fencing) should prioritize conventional seismic lines (i.e. cleared lines used for traditional oil and gas exploration) and pipelines, as they were selected by wolves and increased travelling speed, before low‐impact seismic lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it