Futures Analysis of Urban Land Use and Wetland Change in Saskatoon, Canada: An Application in Strategic Environmental Assessment
Bibliographic record
Abstract
This paper presents a scenario-based approach to strategic environmental assessment (SEA) for wetland trend analysis and land use and land cover (LUC) modeling in an urban environment. The application is focused on the Saskatoon urban environment, a rapidly growing urban municipality in Canada’s prairie pothole region. Alternative future LUC was simulated using remote sensing data and city spatial planning documentation using a Markov Chain technique. Two alternatives were developed and compared for LUC change and threats to urban wetland sustainability: a zero alternative that simulated trends in urban development and wetland conservation under a business as usual scenario, in the absence of prescribed planning and zoning actions; and an alternative focused on implementation of current urban development plans, which simulated future LUC to account for prescribed wetland conservation strategies. Results show no improvement in future wetland conditions under the city’s planned growth and wetland conservation scenario versus the business as usual scenario. Results also indicate that a blanket wetland conservation strategy for the city may not be sufficient to overcome the historic trend of urban wetland loss; and that spatially distributed conservation rates, based on individual wetland water catchment LUC peculiarities, may be more effective in terms of wetland conservation. The paper also demonstrates the challenges to applied SEA in a rapidly changing urban planning context, where data are often sparse and inconsistent across the urban region, and provides potential solutions through LUC classification and prediction tools to help overcome data limitations to support land use planning decisions for wetland conservation.
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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 | 0.000 | 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".