Topology Vulnerability Analysis of several Urban Metro Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In modern cities, urban metro systems gradually become an important transportation tool. The failure of metro may influence citizens’ travel and cause economic losses. It is a focal problem that assessing the vulnerability of metro networks at home and abroad. Several metro networks are modeled by a modified Space L, in which metro interchange and travel time are involved. The properties of these metro networks are calculated at first, showing that at the same size, the average degree is larger, the network efficiency is better. Then the vulnerabilities of metro networks under random attack and three malicious attacks are studied and discussed. It is discovered that the metro networks are vulnerable to the biggest travel-time-efficiency node-based attack(EA) and the highest betweenness node-based attack(BA), and robust against random attack. The four attacks harm Tokyo metro network least, which has a big size, the max average degree and clustering coefficient of the seven metro networks. Finally, the top ten stations in order under EA and BA are respectively listed as a case study of Shanghai metro.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 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 it