A climate indicator dashboard for communicating climate change in the Okanagan Valley of B.C.
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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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".