CORRELATION AND EXTRAPOLATION OF CREEP-RUPTURE DATA: A CASE STUDY USING 9Cr-1Mo STEEL DATA
Bibliographic record
Abstract
The various parametric methods used to extrapolate data from shorter-time creep tests to longer times as required for design purposes are reviewed. The limitations on the applicability of the various methods are discussed, and data obtained from an experimental test program on 9Cr-1Mo steel, together with recent published reference data, are used to demonstrate the variation in the predicted life at longer times on the basis of a series of parametric methods. It is emphasized that the most appropriate parameter for the particular data set and material in question must be used. The choice of parameter should not be made blindly or substantial differences between predicted allowable stress and actual allowable stress can result. The data for the 9Cr-1Mo steel are discussed in terms of creep mechanisms and precipitation effects, and the potential problem of lower stress than predicted for long-term behaviour is examined.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".