Using Bayesian networks to map winter habitat for mountain goats in coastal British Columbia, Canada
Bibliographic record
Abstract
The mountain goat (Oreamnos americanus) is an iconic wildlife species of western North America that inhabits steep and largely inaccessible terrain in remote areas. They are at risk from human disturbance, genetic isolation, climate change, and a variety of other stressors. Managing populations is challenging and mountain goats are particularly difficult and expensive to inventory. As a result, biologists often rely on models to estimate the species’ abundance and distribution in remote areas. We used landscape characteristics evident at point locations of mountain goat visual observations, tracks, and telemetry locations, along with random locations, to learn the structure and parameters of a Bayesian network that predicted the suitability of habitats for mountain goats. We then used the model to map habitat suitability across 285,000 km2 of potential habitat in mountain ranges of the south and central Canadian Pacific coast. Steep slopes, forest cover characteristics, and snow depth were the important drivers. Modeling the system as a Bayesian network provided several advantages over more common regression methods because input variables were heterogenous (i.e., a mix of discrete and continuous), autocorrelated, and animals exhibited non-linear responses to landscape conditions. These common characteristics of ecological data routinely violate the assumptions of parametric linear models, which are commonly used to map habitat suitability from animal observations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".