Analysis of Cascading Effects on Key Urban Networks During Flooding in Brazzaville, Congo
Bibliographic record
Abstract
This manuscript analyses the cascading effects between urban technical networks in Brazzaville, Congo.To this end, we have identified the urban networks that are essential to the functioning of the city, namely the road network, the drinking water network, the sanitation network and the electricity network.The working methodology is based on a spatial analysis of flooding and an analysis of vulnerability using indicators of exposure (direct contact with water), sensitivity (malfunctions caused when in contact with water) and adaptability (continuity of operation once in contact with water) of the urban technical networks.The analyses show that all the technical urban networks appear to be dependent or interdependent on each other.The road network is the most exposed, but causes very little disruption to the others, while the electricity network is the one that causes the most disruption once it malfunctions.The cascading effects between urban technical networks stem from functional, physical or cybernetic dependency and can cause partial or total failure of the affected network.A power failure could extend functional vulnerability to the drinking water supply network via pumps, which depend on electricity.This research is being carried out in the context of urban risk management, with the aim of ensuring urban resilience to flooding.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".