A climate indicator dashboard for communicating climate change in the Okanagan Valley of B.C.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Okanagan Valley in British Columbia, Canada, is increasingly vulnerable to climate change, experiencing hotter temperatures, longer and more intense wildfire seasons, extreme cold events, long-term droughts, and less predictable water supplies. Communities now often experience multiple climate-driven extreme events within the same year. Therefore, the Okanagan Basin Water Board (OBWB), a regional water resource management body, recognized the need to support regional decision-makers with effective tools to integrate local climate context into community-scale planning and communication. However, climate change is complex and regional decision makers are not trained climate experts. An effective decision support tool must therefore provide accurate and relevant information in a transparent and intuitive way. Motivated by this need, this study describes scientific methods and design principles used to calculate, visualize and present over 30 locally relevant indicators developed from publicly available weather and climate observation data on the publicly available OBWB Climate Indicators Dashboard. The process involved identifying useful climate impact indicators, understanding available data sets and their limitations, understanding and building trust with the intended audience, and iterating on data visualization design and dashboard wording for maximum impact. By presenting our methods and design principles, we highlight the OBWB Climate Indicators Dashboard as one among an emerging class of community-scale tools to communicate climate change. Based on initial positive feedback of the tool, we hope our case study is useful to others planning to create their own watershed-scale climate communication tools.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it