A Coupled Approach to Developing Damage Prognosis Solutions
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
An approach to developing damage prognosis (DP) solution that is being developed at \nLos Alamos National Laboratory (LANL) is summarized in this paper. This approach integrates \naadvanced sensing technology, data interrogation procedures for state awareness, novel model \nvalidation and uncertainty quantification techniques, and reliability-based decision-making \nalgorithms in an effort to transition the concept of damage prognosis to actual practice. In parallel \nwith this development, experimental efforts are underway to deliver a proof-of-principle technology \ndemonstration. This demonstration will assess impact damage and predict the subsequent fatigue \ndamage accumulation in a composite plate. Although the project focus will be DP for composite \nmaterials, most of this technology can generalize to many other applications. The unique aspects of \nthis approach discussed herein include: 1) multi-length scale damage models analyzed on tera-scale \ncomputer platforms that discretize composites on an individual lamina level, 2) integration of \nadvanced sensors with Los Alamos’s flight-hardened data acquisition system, 3) damage detection \nbased on a statistical pattern recognition approach, and 4) reliability-based metamodels with \nquantified uncertainty that can be deployed on microprocessors integrated with the sensing system \nfor autonomous damage prognosis.
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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