When is a small park big enough?, effects of size, isolation and human disturbance on mammal species relaxation in Canadian national parks
Bibliographic record
Abstract
Parks Canada's legislation calls for the maintenance of ecological integrity, which by definition includes the historic composition of species. Consistent with research that has used island biogeography theory as a framework for examining faunal relaxation in parks, I found that 23 of 24 (96%) of the national parks studied had lost disturbance-sensitive mammals. Unlike previous research, however, park area was not significantly correlated with species loss. An examination of landscape attributes using Geographic Information Systems (GIS) quantified the amount of human development and habitat within the boundaries, and in a 50 km zone around each of the parks to see whether these correlated with species loss. The areas of impact of human development were estimated using a buffering analysis on built features. Habitat analysis was based on satellite imagery. These variables, together with data on human population and visitor density were significantly correlated (p < 0.05) with loss of disturbance-sensitive mammals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".