Une nouvelle approche pour la caractérisation des aléas et l'évaluation des vulnérabilités des réseaux de support à la vie
Bibliographic record
Abstract
Life support networks are vital structures, as they are significant in size and essential in nature for the entire society. A network failure may thus involve serious consequences. The analysis of the needs of those in charge of emergency measures has established that the behaviour of life support networks had to be studied not only for extreme events but also for all possible situations of failure. In this context, the role of civil engineers is crucial, since they must assure the safety of infrastructures from design to dismantling. The proposed approach consists of characterizing networks according to the importance and efficiency of their missions, operations, and essential infrastructures. This allows approaching network vulnerabilities from an exhaustive and global perspective. Based on the degree of efficiency of a mission, it is possible to identify the dysfunctioning operations and infrastructures, from which the internal and external causes of faults or failures could be defined. Instead of starting from a hazard and asking the question "What-if", as is currently done, this new approach aims at determining the vulnerabilities of a network by asking the question "Why". Once the potentialities of failures are defined, it will be possible, by using a global method of fusion, to determine the vulnerabilities of the various components of a life support network (missions, operations, infrastructures). This paper describes this new approach and the required criteria for the development of a global methodology for the assessment of anthropic hazard. Our article illustrates this approach with an application to a hydroelectric facility.Key words: risk, vulnerability, reliability, life support network, hydropower.
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 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".