Method of Critical Stochastic Inputs for Extreme Uncertainty Problems: Theory and Applications
Bibliographic record
Abstract
Engineering systems in diverse fields of technology are subject to actions of outer environment that cannot, in principle, be predicted with certainty. Observations over short periods of time provide limited information about such factors as height and energy of ocean waves, magnitudes of earthquakes, hurricane wind speed, etc.; however, there is no assurance that the chosen level of action will never be surpassed. This uncertainty that undermines the very basis of the system's design is called "extreme uncertainty". One approach in quantification of the extremely uncertain actions is the method of critical stochastic inputs (MCSI). This method assumes that the maximum magnitude of the action can be assessed with greater certainty and is known; however, the frequency spectra or wave profiles are set based on their worst possible pattern for the given system, with controlled reduction based on probability of exceedance to avoid undue conservatism in the design. The MCSI naturally arises in many applications in various fields of engineering. In the paper, the opportunities of usage of the method in five real-life examples are outlined and the principal theorems of the method are formulated and proven.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".