How do human activities shape wolves' behavior in the central Rocky Mountains region, Alberta, Canada?
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
Wolves (Canis lupus) may be considered an indicator species for cumulative effects induced by human interactions. This paper describes the conceptualization and implementation of an agent-based model to investigate how different intensity levels of human activities affect wolf's behavior in the central Rocky Mountains region of Alberta. Most agent-based models for wildlife study include two components: an animal movement component and a set of environmental data layers that represent attributes of the physical environment over which the animals move. Our model consists of a wolf module as the primary component, and bear, elk, and human modules that represent dynamic components of the wolf's environment. The model was run for six months of the summer from April 16 to October 15 using seven sets of parameters replicated 15 times. The model was calibrated and validated with previously collected radio collared GPS data acquired yearly from 2001 to 2005. The simulated trajectories of wolves reflect similar movement patterns as indicated by the real trajectories. The simulations reveal that the wolves' movement and behavior are significantly affected when increasing the intensity of human presence. The modeling prototype developed in this study may serve as a useful tool to test hypotheses about human-wildlife interactions and guide decision makers in designing adequate management strategies.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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