Utilizing Copernicus and Google Earth Satellite Imagery to Define Habitat Suitability for Flying Squirrels (Glaucomys spp.) in the lower Rouge River basin, Toronto, Ontario
Bibliographic record
Abstract
Habitat loss is the leading threat to species populations. With urbanization expanding across the landscape habitats are continuously being fragmented, leaving populations vulnerable to local extinction. It is important to assess the effects of fragmentation by determining whether current habitat conditions are suited to sustain species populations. As flying squirrels are indicators of forest health, I looked at identifying habitat conditions for northern flying squirrel species (Glaucomys sabrinus) in the southern region of Rouge National Urban Park. In this study, I classified coniferous forests through the methods of supervised classification and segmentation using ArcGIS as an attempt to improve the current provincial land cover map, SOLRIS. Coniferous forest cover maps were created using Google Earth imagery from December 30, 2005 and April 08, 2016 along with Copernicus satellite imagery from March 23, 2019. Results were mixed, with conifer classification for the two Google Earth images showing higher success based on the 2005 Google Earth image, while Copernicus results revealed greater conifer classification success based on the 2016 Google Earth image. Habitat viability of northern flying squirrels was compared between the pre-existing SOLRIS land cover map and the 2005 based Copernicus conifer cover map using metapopulation modeling. Results indicate that differences in population dynamics exist between the two maps with the Copernicus conifer cover map revealing greater habitat viability, however further research based on a better parameterized model is required to understand habitat conditions and viability of northern flying squirrels in southern Ontario.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".