The Process of System Collapse Based on Areas of Vulnerability
Bibliographic record
Abstract
The purpose of this paper is to introduce a new approach to the process of system collapse based on areas of vulnerability. For this purpose, a new category of contingencies called abnormal contingencies and a new system operating state called verge-of-collapse are proposed. The application of these abnormal contingencies to the vulnerable areas can lead to a system on the verge-of-collapse. In particular, there are two conditions that generally occur for a system to be on the verge-of-collapse: firstly, the system is in a degraded state or close to its operation limits and secondly it must be subjected to abnormal contingencies in the areas of vulnerability. The result of this study is to define areas of vulnerability in a network which can then be identified and appropriate remedial action initiated when required. Different stages are required for the protection of systems on the verge of collapse, namely: a) how to define areas of vulnerability, b) how to identify these areas of vulnerability and c) how to protect the system in the presence of areas of vulnerability. This paper is the first stage in proposing a new approach to the protection of systems on the verge of collapse
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".