Human disturbance alters the predation rate of moose in the Athabasca oil sands
Bibliographic record
Abstract
Abstract Human disturbance can alter predation rates to prey in various ways. Predators can use human disturbance to facilitate hunting, thereby increasing exposure to prey. Conversely, when predators avoid human disturbance and prey do not, prey refugia are generated. Because the direction and magnitude of such effects are not always predictable, it is important to examine if and how predation rates vary with human disturbance. Alberta's Athabasca oil sands region (AOSR) is a region of boreal forest characterized by extensive human disturbance and is home to moose (Alces alces) and wolf (Canis lupus) populations. We examined whether the wolf predation rate of moose varies with human disturbance in theAOSR. We compared the distribution of wolf kills of moose to a spatial index of moose density in uplands and wetlands, near and far from rivers, mines and facilities, and at high and low densities of linear features near human habitation. Moose were killed closer to mines and rivers and at lower densities of linear features than expected by random. When compared to the relative availability of habitats, more kills of moose occurred in upland forest than in wetlands. However, when compared to the relative density of moose, kills only increased with decreasing distance to mines and rivers. We conclude that predation rates of moose have increased near human disturbance inAOSRbecause the mining footprint has removed habitat causing changes to the intensity of wolf use of areas near the boundary of mines. We discuss possibility of sink habitat near mining features and whether that is expected to reduce moose population density acrossAOSR.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".