Building adaptive capacity and climate change resiliency in rural communities
Bibliographic record
Abstract
Resilient rural communities are not only those that can adapt to changing socio-economic conditions, but are also plan for and adapt to a changing climate. Using a guidebook developed by the Canadian Model Forest Network, Black River First Nation (Manitoba) undertook a 3-year project to address risks to the community and their traditional area posed by climate change. The project involved a community core team which documented their observations of changes in climate and its impact on the community and traditional area over the last 50 years, developed historic and current climate profiles from meteorological data in the region, used climatic global circulation models to predict changes in climate in their traditional area to the year 2080 and assessed current and future risks. Based on the vulnerability assessment, the community developed an action plan to adapt to current and future changes in climate. Actions included updating their emergency preparedness and response plan, developing wildfire protection plans for the community and a nearby cottage subdivision they are planning, and upgrades to community infrastructure (drinking water and sewage treatment), among others. As a result of the project, more than $15 million has been invested in making the community more climate-resilient.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".