An agent-based model of woodland caribou habitat selection in west central Alberta: A behavioral and ecological approach
Bibliographic record
Abstract
Alberta woodland caribou (Rangifer tarandus) are classified as threatened in Canada, and a local population in the province’s Foothills Region, the Little Smoky herd, is at immediate risk of extirpation due, in part, to anthropogenic activities such as oil, gas, and forestry that have altered the ecosystem dynamics. The Alberta government resultantly recommends the assessment and management of cumulative effects on caribou, as well as the identification and provision of adequate habitat (amount and type), to allow for caribou persistence (ASRD 2010). While much is known about caribou ecology, the behavioural mechanisms by which resource-extraction industries contribute to caribou population decline are less clear. Traditional approaches to studying wildlife-human- environment interactions do not typically consider individual-level information, account for complexities, or integrate cross-scale and cross-discipline data and methods, resulting in a great loss in predictive or explanatory power (An et al. 2005). To address these issues, we have developed a spatially explicit, agent-based model (ABM) to simulate winter habitat selection and use of woodland caribou in the face of intense land use by resource-extraction industries in west-central Alberta.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".