Applying the Anticipatory Failure Determination at a Very Early Stage of a System’S Development: Overview and Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Anticipatory Failure Determination (AFD) is a tool used in the TRIZ (Theory of Inventive Problem Solving) methodology. This article introduces its concept and describes the process of AFD in different versions of the method. The article presents the application of the AFD method at a very early state of a system’s development, i.e. its concept formulation stage, which corresponds to a technology readiness level (TRL) equal to 2. The system under analysis is a set of devices used to reduce displacement ship hull resistance. The system was modelled using functional analysis. An analysis of system resources was then carried out. Possible direct, indirect, and accident-related failures were identified. A multi-criteria analysis of the causes of system failures was conducted from which the top 10 potential failures were selected. Observations were made on the applicability of AFD in respect to systems not yet implemented.
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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