Ecological, Shade, and Soil Connectivity on the University of British Columbia Vancouver Campus : How can connectivity be identified to better allocate natural assets?
Bibliographic record
Abstract
In response to growing concerns about the impacts of climate change, recent studies have been conducted investigating ecological connectivity and resiliency. Improving connectivity can increase landscape resiliency in the face of a changing climate. The University of British Columbia (UBC) Vancouver campus is interested in improving its ecological, soil, and shade connectivity, as these factors influence student and environmental health. Satellite imagery, and data from UBC Campus and Community Planning was used to analyze ecological (specifically important endemic tree species), shade and soil connectivity on the Vancouver campus. Ecological connectivity was lower in the north eastern portion of campus, specifically there were fewer trees and they were more spread out. Shade connectivity was higher in the north part of campus where there were buildings and trees. Shade connectivity was the lowest in the southern part of campus where there were more open fields. Soil connectivity was the highest in the southern part of campus, as there were more fields. Soil connectivity was the worst in the north section of campus as there was more linear infrastructure and buildings. Planting tree species that are more suitable for future climates and maintaining high levels of connectivity will improve landscape resiliency. To maintain the history of a landscape, a portion of land can be reserved for endemic species where their connectivity is maintained to a high level. Increased maintenance could help endemic species survive, and limiting the species to a section of land would keep maintenance costs as low as possible. Improving soil connectivity would improve soil health, allowing for more and healthier trees, which in turn would improve shade connectivity, helping to maintain thermal comfort. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".