Climate Change Adaptions for Urban Water Infrastructure in Jeddah, Kingdom of Saudi Arabia
Bibliographic record
Abstract
Cities play a crucial role in the planning of climate change adaptions. Although these actions are largely guided by global negotiations and national policies their consequences are usually felt by individual cities. Reconfiguring of urban infrastructure is the first step to ensure resilience to extreme weather events triggered by climate change. Many coastal cities are already begun to suffer because of climate change impacts; frequent flooding in Jeddah (Kingdom of Saudi Arabia) is an example. This paper attempts to investigate preparedness of urban water infrastructure in Jeddah for future climate change adaptions. It founds that the city has been lagging behind in action such as continuous & consistent reporting of relevant data, capacity building, research, education and awareness building, reconfiguration and expansion of grey & green infrastructure. We propose to formulate a three point policy for climate change adaption at local level with proper attention to grey, green and soft infrastructures.
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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 | 0.001 | 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 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".