[Re]covering Jeddah’s Wadis – Building the City’s Resilience through Green Infrastructure
Bibliographic record
Abstract
Jeddah, Saudi Arabia’s largest coastal city, is positioned between two prominent natural features: the mountain range on its eastern side and the Red Sea on its west. The city faces many challenges central to which is storm water drainage. The natural drainage of the city through its pre-existing wadis, bringing down the rain water from the steep mountain ranges through the low inclining coastal plane and into the sea, has been interrupted in the last few decades by massive road infrastructural projects cutting through the city and interrupting the natural flow. The outcome of these interventions has been excessive flooding calamities, of which the ones in 2009 and 2011 were the most extreme, causing severe damage to infrastructure, property and lives.In light of climate change the intensity of flash floods is expected to increase, placing enormous stress on the city. To control the floods the city has pushed forward heavily engineered solutions, canalizing the rich network of wadis, almost 80 in number, into 4 major concrete channels that discharge the rain water accumulated in the mountains directly into the sea. This solution, which has been prohibitive in cost, has robbed the city of any potential of utilizing the precious supply of rain water. This paper explores the potential of recovering Jeddah’s wadis and creating green corridors across the city. As opposed to engineered solutions which address singular problematics, green infrastructures could provide numerous benefits to the city and the region as a whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".