Successful Round Robin Analyses Resulting from the Engineered Residual Stress Implementation Working Group
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 The application of engineered residual stresses (ERSs) on aircraft structure provides an opportunity to significantly extend the total fatigue life of critical components. In order to reach required service life goals within budgetary constraints, the ability to implement ERS into analyses is essential. However, it has been repeatedly demonstrated that in order to properly quantify, apply, and analyze ERSs, sophisticated analytical tools, advanced technical knowledge, and specialized training are required. The ERS implementation (ERSI) working group provides the opportunity for collaborative development of best practices for government, contractors, and engineers supporting the implementation of ERSs into life predictions. The ultimate goal of the working group is to develop a more holistic framework for the implementation of ERS, with validated tools and processes for application to aircraft structures, minimizing expensive test programs, and offering benefits to all stakeholders. The ERSI Fatigue Crack Growth Analysis Method committee has taken the initiative to develop round robin fatigue life predictions for cold expanded holes. An initial round robin effort was completed to quantify the epistemic uncertainties in the prediction of fatigue crack growth, given a fixed set of input data. The results of this round robin are presented, including the variations in the predictions and comparison with test results, as well as lessons learned and best practices.
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.
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