Bibliographic record
Abstract
The degree to which predator and prey distributions overlap in space influences the probability of encounters between predator and prey, kills of prey, and consequently, how each species’ abundance varies in time and in space. Predator and prey attempt to increase or decrease overlap respectively through movement and habitat selection, processes that are sensitive to habitat heterogeneity. If predator and prey respond differently to novel habitat heterogeneity such as a zone of influence in and around human disturbance, it may provide prey with a refuge or facilitate predator hunting efficiency. Alberta’s Athabasca oils sands region (AOSR) is a region of boreal forest with extensive mining developments and overlapping wolf (Canis lupus) and moose (Alces alces) populations. To assess whether the human disturbance in AOSR has affected wolf-moose spatial overlap, I quantified the degree to which both wolves and moose avoid human disturbance across my study area. I hypothesized that wolves would avoid areas disturbed by human developments and activity, and that this avoidance would be used by moose as a refuge. Wolves and moose both used and selected areas near human disturbance such that no refugia for moose was available due to human disturbance. Further, I found that a higher proportion of moose were killed as the distance to oil sands mines decreased. I also found that wolves selected to move on linear features associated with oil extraction and such selection facilitated faster movement. Wolves did select to move farther away from human habitation and oil sands facilities, but only during the day. There was no relationship between wolf movement speed and proximity to industrial facilities, urban area or oil sands mines. Moose cows, particularly those with calves, strongly avoided areas within their home ranges with a high intensity of wolf use. In addition, moose altered their behaviour both within and between individuals as a function of the local intensity of use by wolves, but only with respect to natural features. Rivers and streams were avoided in areas with more wolf use. Overall, I conclude that human disturbance in AOSR has not generated prey refugia for moose, rather it has provided a marginal advantage for wolves while hunting in proximity to mines.
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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 | 0.001 |
| Open science | 0.001 | 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".