Closure to “Discussion of ‘Damage Modeling in Random Short Glass Fiber Reinforced Composites Including Permanent Strain and Unilateral Effect’ ” (2006, ASME J. Appl. Mech., 73, pp. 347–348)
Bibliographic record
Abstract
If we compare the first equation of the Discussion to Eq. (2) of the paper, we can remark that the authors of the Discussion have misunderstood the former equation,12Eσ̃ii+σ̃ii+≠12Etr[(σ̃+)2]12Eσii−σii−≠12Etr[(σ̃−)2]Considering this, their remark is irrelevant. Nevertheless, we can observe easily that in the particular case [D]=0, Eq. (2) is simplified to Eq. (1) of the paper,Ue(σ,[D]=0)=12E(∣σii+σii++σii−σii−+σijσij∣i≠j)−υE(σiiσjj−σijσij)=12E[σ]:[σ]−υE((tr[σ])2−tr([σ]2))Furthermore, we can observe that if we put [D]=0, the fourth-order damage operator defined in Eq. (6) becomes [M]=[I4]. Thus, in this particular case, Eq. (9) is exactly equal to the classical complementary energy presented at Eq. (1).The authors of the Discussion mentioned that “when the principal axes of the damage tensor do not coincide with the one of the stress tensor,” some relations presented in the paper are not applicable. We agree with this affirmation because we mentioned it in Sec. 2.3 of the paper and in the Conclusion. When we spoke about proportional loading, this means that the principal stress directions, and thus, those of damage tensor, are identical and do not change during loading. We apologize not having mentioned it more clearly at the beginning of the paper.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".