BBN-Based Reasoning Approach for Safety Verification Using FSN
Why this work is in the frame
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Bibliographic record
Abstract
In process industry, there are uncertainties associated with each variable, which might lead to process deviations and hazards. In order to accurately quantify the risks associated with these hazard scenarios, quantitative probability should be calculated. The process dynamically changes during plant operation, which requires continuous monitoring of process risks and real-time safety verification. It is challenging to both dynamically and instantaneously estimate the risks for all faults and deviations. An FSN is introduced in this paper to systematically and continuously estimate risks for all possible fault propagation scenarios. Intelligent reasoning algorithms are proposed using a BBN to accurately estimate risks. An FSN is used to analyze causes and consequences of different faults using automated forward and backward propagation learning techniques. Real-time safety verification is applied to each fault propagation scenario. The TE process is used to illustrate the proposed real-time safety verification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it