Preliminary hazard analysis for the design alternatives based on fuzzy methodology
Bibliographic record
Abstract
A fuzzy logic approach is adopted in order to add more power features and update a classic hazard assessment methodology. Fuzzy logic provides an easy tool for directly manipulating the linguistic terms used in the risk assessment. Based on the findings for the four-stroke diesel engine safety assessment there are five metrics of the consequences categories defined as follows: Cost and Equipment Damage, Operability, Maintainability, Personnel Death/Injury and Environmental Impact. There are four likelihood categories defined by the linguistic expressions as follows: likely, may occur, not likely and very unlikely. The quantitative ranges of the consequences and likelihood categories are adopted to create the universe of discourse for the fuzzy sets. The Risk Ranking Matrix is adopted to create the fuzzy sets rules for each combination of severity of consequence and likelihood for five metrics. This study has confirmed that fuzzy methodology is one technique that is particularly appropriate for processing the choice of best alternative in the early stage of the design. The fuzzy method shows clearly the advantages of the fuzzy ranking matrix in comparison with the previous analysis performed by classic hazard assessment methodology.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".