Analysis of Habitat Fragmentation and Ecosystem Connectivity within The Castle Parks, Alberta, Canada
Bibliographic record
Abstract
Habitat fragmentation is an important subject of research needed by park management planners, particularly for conservation management.The Castle Parks, in southwest Alberta, Canada, exhibit extensive habitat fragmentation from recreational and resource use activities.Umbrella and keystone species within The Castle Parks include grizzly bears, wolverines, cougars, and elk which are important animals used for conservation agendas to help protect the matrix of the ecosystem.This study identified and analyzed the nature of habitat fragmentation within The Castle Parks for these species, and has identified geographic areas of habitat fragmentation concern.This was accomplished using remote sensing, ArcGIS, and statistical analyses, to develop models of fragmentation for ecosystem cover type and Digital Elevation Models of slope, which acted as proxies for species habitat suitability.Data indicated that the primary threat to the study species was increased habitat fragmentation caused by an increase in dirt roads and lightly-used trails.Identifying each species' habitat needs, alongside considerable fragmentation areas allowed for the development of recommendations to mitigate the negative effects of fragmentation, and thus better conserve the ecosystem in these provincial parks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".