The Vulnerability of Mobility to Industrial Risks in Annaba, Algeria
Bibliographic record
Abstract
This article deals with the vulnerability of transport networks in the event of an industrial accident spreading outside the fertilizer production site of the Fertial (Fertilizing Algerian) complex in the city of Annaba, Algeria. In this study, an accident simulation (ammonium nitrate explosion) was used to anticipate the possible impact of an explosion on transport infrastructure users, particularly the road network, in the city of Annaba. It is important to note that according to the latest data collected by the BETUR study office and field observations, 80% of the population’s daily movements are carried out by the road network. To achieve these objectives, the methodology adopted was based on the use of a road traffic simulation model (4-stage model) using TRANSCAD software. The data obtained made it possible to project and map the average daily traffic (MTR) on all structural roads in the municipality of Annaba. The results of this first phase were integrated into a database using a geographic information system (GIS) to perform a cross-referenced spatial analysis with the impact areas caused by the explosion of a certain quantity of chemicals from fertilizer plant stocks (ASMIDAL). The results highlight the vulnerability of the municipality of Annaba to major industrial accidents (comparable in scale to similar accidents in Toulouse in 2001 and Beirut in 2020). The superposition of the results of the traffic simulation and the impact zone of the explosion shows that the proportion of road traffic confronted with the initial consequences of the explosion (blast, blast and boil-over) is essential. Consequently, authorities must rapidly take charge of the territory’s vulnerability to industrial disasters. Finally, it is important to note that road traffic is a part of a complex system composed of several elements (population, infrastructure, buildings, environment, etc.).
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".