Different Phenomena Encountered during Dilatometry of Low-density Steels
Bibliographic record
Abstract
Abstract In recent decades, highly alloyed low-density steels are being developed to reduce the weight of different automotive parts. Dilatometry can be a very useful experimental technique to understand phase transformations during heating or cooling of new low-density steel alloys. When performing dilatometry measurements some assumptions are made such as the homogeneity of the sample material tested during the experiment. In this study, dilatometry tests were performed for two different low-density steels, and the variations of the composition between the surface and the inner part of the sample were analyzed. The migration of manganese by diffusion from the interior of the samples and finally its evaporation on the surface under vacuum were observed. This compositional gradient generated in the samples may influence the veracity and interpretation of the results obtained in dilatometry when working with high manganese steel alloys. The detachment of surface grains created by this compositional change near the surface of the samples is also investigated.
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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".