Five Municipal Case Studies on Adapting to Climate Change for Professional Planners
Bibliographic record
Abstract
From 2002 to 2006, the Earth Sciences Sector of Natural Resources Canada implemented a program entitled "Reducing Canada's Vulnerability to Climate Change". Within this program, one project, "Municipal Case Studies: the planning process and climate change" aimed to generate scientific knowledge on a sample of the major climate change impacts facing Canadian communities. One of the desired outcomes of this project was that professional planners would use geoscientific information in planning for climate change. The case studies were selected to address issues such as water resource depletion, coastal erosion due to higher sea levels, and permafrost melting. In order to make the results of the case studies accessible to the planning community, City Spaces Consulting Ltd., a planning consulting firm based in Victoria, British Columbia, was contracted to produce summary reports of the five case studies and draw out the planning implications of the results. This Open File presents these summary reports in both official languages. We would like to acknowledge the contributions of the numerous scientific collaborators and community participants in these studies that are listed in the individual reports. Natural Resources Canada scientists led two of the case studies (Calgary: Steve Grasby; Delta: Phil Hill). The other three were led by the University of Victoria (Graham Island: Ian Walker), Université Laval (Salluit: Michel Allard) and Environment Canada (New Brunswick coast: Réal Daigle) with NRCan scientist participation.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.013 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".