Supporting Local Climate Change Adaptation with the Participatory Geoweb: Findings from Coastal Nova Scotia
Bibliographic record
Abstract
The effects of climate change have been detected in various natural systems in the last century (IPCC, 2014). As a consequence of these changes, governments are seeking to identify adaptive strategies for protecting citizens and vulnerable economic sectors (Adger et al., 2005; Smit & Wandel, 2006). Coastal areas are particularly susceptible to a changing climate as sea level continues to rise and storm surges become more powerful and frequent events. This research introduces the use of the participatory Geoweb as a tool (labelled “AdaptNS”) for supporting local climate change adaptation efforts in Shelburne County, Nova Scotia. AdaptNS serves as a visualization tool for displaying high-resolution interactive flood maps of sea level rise and storm surge scenarios between 2000 and 2100. The participatory aspect of this Geoweb tool is integrated as a means for decision-makers, stakeholders, and community members to identify adaptation priorities in response to climate change risks. This interdisciplinary approach was possible through the use of several technologies, including the Arcpy Python library for analysis, and a coupling of the Google Maps API and LAMP bundle for the front-end and back-end tool development. By using feedback from community members, AdaptNS was identified to support local adaptation by providing communities with comprehensive visuals of climate change risks, a platform for identifying adaptation priorities, and a means to communicate local risks to upper levels of government and businesses.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".