An Integrated Approach for Evaluating Adaptation Options to Reduce Climate Change Vulnerability in Coastal Region of the Georgia Basin
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper presents an integrated approach that integrates climate change impact assessment/vulnerability identification, adaptation option evaluation, and multi-stakeholder participation. The integrated approach was applied in the Georgia Basin (GB) for identifying desirable adaptation options to reduce climate change vulnerabilities. Different computer-based and non-model based methods were adopted to form the integrated approach. These tools include environmental simulation modeling, geographical information system (GIS), internet multi-stakeholder consultation, and multi-criteria decision making (MCDM). The research started with the identification of vulnerabilities of ecosystems, coastal areas, and economic sectors to climate change. This was followed by an online survey and interviews that allow stakeholders to conduct a multi-criteria evaluation of adaptation options. The analytic hierarchy process (AHP), an MCDM technique, was adopted to develop an adaptation evaluation tool to identify the priorities of sustainability goals/indicators and to rank desirability of adaptation options. The case study in the Georgia Basin of Canada provides some articulation on how the integrated approach can provide an effective means for the synthetic evaluation of the general desirability levels of a set of adaptation options through a multi-criteria and multi-stakeholder decision making process. Thus, the study contributes to the science on adaptation option evaluation. While the case study identified and evaluated a number of adaptation options to deal with potential vulnerabilities to climate change in several key sectors in the region, this paper focuses on sea level rise (SLR) impacts and adaptation options for the coastal region management. The completed research results of the case study are described in the final report submitted to Climate Change Action Fund of Canadian Government (Yin, 2001).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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