Integrating ecological infrastructure in regional planning: a methodological case study from the Calgary region of western Canada
Bibliographic record
Abstract
The Calgary region of south western Alberta, Canada, like other areas of western North America, is experiencing dramatic population growth. The cumulative effects of rapid urbanization and land use intensification, specifically related to water use in a semi-arid region, are poorly understood. But, this needs to be considered in sustainable land use planning and policy development at a regional scale. There is a growing awareness among the municipalities in the Calgary area that a coordinated inter-municipal 'partnership' approach is needed to address long term regional growth management. We present an innovative methodology to incorporate landscape ecology and ecological infrastructure into strategic policy planning for regional development. Our approach involves the identification of critical ecological infrastructure related to landscape hydrology, the development of ecological performance criteria and preferred spatial development patterns related to landscape heterogeneity and connectivity and ecological infrastructure capacity. The methodology incorporates current urban ecology and landscape ecology thinking and encompasses both the 'gray' and 'green' infrastructure needs necessary to support regional population growth patterns. Three methodological tools are used to spatially 'link' ecological infrastructure performance, landscape heterogeneity and land use change over time. The methodology will be coupled with cellular automata scenario modelling at a watershed scale. The paper demonstrates key principles by focusing on two critical ecological facets of the Calgary area's regional landscape: landscape connectivity and landscape hydrology.
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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 | 0.001 | 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".