Exploring climate change, ecosystems and well-being connections: lessons learned from the application of a geospatial knowledge exchange tool in the Nechako watershed
Bibliographic record
Abstract
The Nechako Watershed is a large system in northern BC that exemplifies the challenge of integrating information across climate, environment and well-being. This research responded to this need with the goal of enhancing how information about the Nechako Watershed is communicated and shared. Informed by the development of a geospatial ‘portal’ tool in northern BC, this research sought to establish the Integrated Watershed Research Group (IWRG) Portal, gain insight on establishing accessible knowledge exchange strategies, and identify its perceived benefits and limitations. The research had two phases. Phase I involved working with a development team to understand this tool, testing, establishing the IWRG Portal, finding and formatting content. Phase II brought members of the Portal User Research Group together to further refine the IWRG Portal and content through scoping discussions, workshops, and a focus group. Thematic analysis was used to code and analyze the transcribed focus group. The research identified benefits of the portal with how it dealt with complexity and its integrative features. Limitations were also found, including the need for intentional framing of data, a steep learning curve, and the need for an internet connection. Analysis also identified the need to tailor content for specific audiences. The research has shown that tools such as the IWRG Portal can create new pathways to understanding and finding information. The research has also identified paths for further refinement and development of the portal tool by expanding the user base and continuing to evaluate the effectiveness of this tool in various contexts.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".