An Assessment of Habitat Connectivity Between Canada’s Protected Areas
Bibliographic record
Abstract
Habitat connections between protected areas (PAs) are important for maintaining animal \nmovement, gene flow, and the long-term viability of wild populations. In this thesis, I assess the \nconnectivity of Canada’s PAs using two approaches. First, I created the most comprehensive \ndataset of Canada’s PAs to date to identify habitat connections and assess differences in the \nconnectivity between government PAs, Indigenous managed lands, private PAs, and randomly \nsampled lands (a control group). Second, using this connectivity information, I applied graph \ntheory to describe the overall spatial configuration of PA networks within each ecoregion of \nCanada. My analysis highlights differences in connectivity between PA types, finds that \ngovernment PAs followed by private PAs have the highest degree of connectivity out of all PA \ntypes, and demonstrates how graph theory metrics can be used in a simple analysis to describe \nimportant characteristics of PA networks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".