Land Development in and around Protected Areas at the Wilderness Frontier
Bibliographic record
Abstract
Protected areas' chief conservation objectives are to include species within their boundaries and protect them from negative external pressures. Many protected areas are not achieving these goals, perhaps in part due to land development inside and outside protected areas. We conducted spatial analyses to evaluate the ability of Canadian protected areas to mitigate the effects of nearby land development. We investigated correlations of national patterns of land development in and around protected areas and then examined national patterns of roads, urban area, and croplands in protected areas. We calculated the amount of developed land in protected areas and within 25-100 km of protected-area borders, the density of roads, and extent of urban and cropland area in protected areas. We constructed logistic-regression models to test whether development in a protected area was associated with landscape and protected-area characteristics. Land development was far less extensive inside than outside protected areas. However, several protected areas, particularly small southern areas near small urban centers had substantial development inside their boundaries, and nearly half of protected areas had roads. The cumulative extent of development within 50 km of protected areas was the best predictor of the probability of land development in protected areas. Canadian First Nations, industries, government, and nongovernmental organizations are currently planning an unprecedented number of new protected areas. Careful management of areas beyond protected-area boundaries may prove critical to meeting their long-term conservation objectives.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".