How do human activities shape wolves' behavior in the central Rocky Mountains region, Alberta, Canada?
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".