Bibliographic record
Abstract
Accurate assessment of system reliability with limited or insufficient statistical data is difficult. This paper presents a method which overcomes the drawbacks of traditional fault tree analysis (FTA) by using FTA based on possibilistic measures and fuzzy logic. This method is designed specifically for situations wherein reliability and safety assessment is imprecise by nature and necessary statistical data is scarce. Based on fundamentals of fuzzy logic, failure possibility is first defined and then fuzzy variables are characterized in the context of possibility theory. Next, subevents in FTA described with natural language are viewed as a collection of elastic constraints of fuzzy variables. Fuzzy rules are generated from linguistic quantification and meaning inference in fuzzy logic. Lastly, an example is used to illustrate the proposed analytical method and reasoning mechanism. Unlike previously reported fuzzy FTA or fuzzy logic-based FTA, this method is an integration of the possibilistic approach and the fuzzy logic-based reasoning approach which is of potential value for creating expert knowledge databases. It can also be applied to other aspects of reliability engineering, addressing ambiguous and subjective uncertainty problems qualitatively and quantitatively.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".