A Composite Graph Theoretic Approach to Modeling Landscape Connectivity for Wildlife Movement in Western Canada
Bibliographic record
Abstract
Connectivity among the resource patches that provide the wildlife with essential habitat is critical to their survival, but highways and other anthropogenic developments commonly impede wildlife movement. The purpose of this study was to identify suitable locations for highway crossing structures for wildlife movement in a fragmented landscape. Functional connectivity was modeled using human footprint data over a regional landscape in western Canada. A graph-theoretic approach was employed to identify corridors, link-age zones, and the locations where wildlife species cross the highways. A betweenness centrality model was used to compute the shortest path, current flow, and network flow of movements across various landscape lattices. The shortest path model identified a set of geodesic paths to connect the resource patches, the current flow model identified a number of movement zones around the resource patches, and the network flow model identified linkage zones in the network. Finally, a composite of the outputs was used to identify suitable locations for highway crossing for maintaining wildlife movement on the landscape.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".