Bibliographic record
Abstract
The Larson–Miller constant (C) of martensitic steel, which is used as the Larson–Miller parameter, is considerably larger than the typically used value of 20 for many types of heat-resistant steels. To provide better understanding regarding this fact, an analytical formulation for the Larson–Miller constant is developed using a model based on interactions between moveable dislocations and elastic singularities in a system. To verify the proposed equation, eight types of Grade 91, 92, and 122 steels are used, whose maximum rupture life exceeds 1E5 h. Creep data are classified into 257 groups by temperature, stress, and strain or time. C values are obtained by applying multiple-regression analyses to time parameters that obey the exponential law, assuming a thermally activated process. C and Ccal are calculated for each data group based on an exponential law and a proposed equation, respectively. The statistical values for C and Ccal are as follows: ̄C=32.41, Cmin=7.87, Cmax=64.88, and (Ccal⁄C)=99.3%. Although (∆C)̄=(Ccal-C) ̅=0.02 is extremely low, the standard deviation of ∆C is large, i.e., 1.27. Results confirmed that the proposed equation can estimate wide-ranging C values, although the equation is expected to be improved. A major component of C for C>15 is an increase in the entropy change caused by elastic interactions between moveable dislocations and elastic singularities in a system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".