Mainstreaming Climate Change for Extreme Weather Events&Management of Disasters: An Engineering Challenge
Bibliographic record
Abstract
Frequency and severity of some extreme weather events are increasing. They are causing extensive damages to infrastructures and to socio-economy of many countries. The loss of human lives due to the extreme weather events are also on the rise. Most of the economic losses occur in the developed countries while most of casualties happen in developing countries. While extreme weather events damage infrastructures, the latter have roles in increasing economic damage and human losses. Failure of levees during Hurricane Katrina and resulting damage is a recent example. Climate change may increase magnitude and frequency of extreme weather events. Infrastructures that already built by taking into account historical climate data will be at a higher risk. New infrastructures need to take into consideration of climate change related parameters that will reduce vulnerability as well possibility of a disaster. However, it is not an easy task. Many policy, design and financial issues are involved. Mainstreaming of climate change in infrastructure planning, design and implementation will emerge as a formidable engineering challenge.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".