Mapping Connectivity: Integrating Ecological Landscape Analysis into Urban Planning in the City of Nanaimo
Bibliographic record
Abstract
Ecosystem-based approaches to urban planning and management are essential for promoting sustainable community development. One of the most significant impacts of urbanization to biodiversity is land conversion, which reduces natural habitats and disrupts ecosystem functionality. Ecological networks, including corridors and green spaces, are critical for maintaining biodiversity, yet connectivity planning must balance ecological sustainability with urban growth and socio-economic considerations. This study had two main objectives: (1) identify from the literature which landscape, ecological, land use, and infrastructure considerations are important for an analysis on landscape connectivity analysis done to support local planning, 2) identify areas within the landscape matrix that may be critical for conserving and restoring connectivity in the City of Nanaimo, and 3) identify the land-use and infrastructure factors that present considerations for the prioritization of the potential connectivity areas. The research followed a four-stage process: first, a systematic literature review to identify ecological and socio-economic variables relevant to urban connectivity; second, the calculation of landscape metrics to assess the configuration and composition of the urban landscape; third, least-cost path analysis to identify potential ecological corridors; and finally, the application of circuit theory to evaluate the relative importance of identified corridors and key areas. The research produces insights that can be used for integrating ecological connectivity into urban planning and highlight the importance of balancing conservation priorities with socio-economic realities in rapidly urbanizing environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".