GIS-Based Climate Change Adaptation Decision Support Tool (ADST): Indices to Assess Agricultural Vulnerability
Bibliographic record
Abstract
There is a need to address the issue of how climate change and water resource and agricultural impacts assessment can be transformed into useful information for use by agricultural decision-makers to reduce climatic risk in agricultural production and explore approaches to bridge the gap between the assessment process and decision-making endpoints. A GIS-based Climate Change Adaptation Decision Support Tool (ADST) can help agriculture decision-makers in considering climate change adaptation in future plans and strategies. This tool builds up a few GIS databases covering key climate-water-agriculture information under current and future climate (2050s) along with a list of agricultural adaptation options. For the case study area - Ningxia, China - a series of ADST indices were computed for describing agro-climatic vulnerability, agricultural adaptive capacity and an agricultural investment plan. The approach to developing the vulnerability maps is based on the IPCC definition of vulnerability as a function of adaptive capacity, sensitivity, and exposure. Indices of adaptive capacity, climate and non-climatic sensitivity, and climate change exposure were constructed to examine the vulnerability to climate change in the region. The analytic hierarchy process (AHP) method is used to prioritize indicators to assess the potential contributions of various aspects to systems' coping capacities. Adaptive capacity for agriculture is considered to be an outcome of biophysical, socio-economic, and technological factors. Climate exposure is determined through the use of scenario results from the Hadley Center's PRECIS regional climate model and Environment Canada's scenarios network (CCCSN).
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".