ETMA: An Efficient Tool for Event Trees Modeling and Analysis
Bibliographic record
Abstract
Event Tree (ET) analysis is a widely used forward deductive safety analysis technique for decision-making at a system design stage. Existing ET tools usually provide Graphical Users Interfaces (GUI) for users to manually draw system-level ET diagrams, which consist of nodes and branches, describing all possible success and failure scenarios. However, these tools do not include some important ET analysis steps, e.g., the automatic generation and reduction of a complete system ET diagram. In this paper, we present a new Event Trees Modeling and Analysis (εTMA) tool to facilitate users to conduct a complete ET analysis of a given system. Some key features of εTMA include: (i) automatic construction of a complete ET model of real-world systems; (ii) deletion/reduction of unnecessary ET nodes and branches; (iii) partitioning of ET paths; and (iv) probabilistic analysis of the occurrence of a certain event. For illustration purposes, we utilize our εTMA tool to conduct the ET analysis of a protective fault trip circuit in power grid transmission lines. We also compared the εTMA results with Isograph, which is a well-known commercial tool for ET analysis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".