Bibliographic record
Abstract
Mg creep resistant alloys have seen increased interest in the 1980s and 90s due to the weight reduction objectives of automotive companies. Development of Mg-Al based alloys with rare earth (RE) and alkaline earth element additions has led to the automotive application of Mg-6Al-2Sr (AJ62) alloy in the BMW engine block and the Mg-4Al-4RE (AE44) alloy in the engine cradle of the Corvette in 2002-2004. Most creep resistant Mg alloy development activities during this period emphasised the creation of stable grain boundary intermetallic phases in the cast microstructure. This elevated the creep performance of automotive Mg alloys to higher temperature and stress combinations (175°C, 70 MPa). Further improvement in creep performance can only arise from an in depth understanding of the creep mechanisms and the related microstructural interactions in Mg alloy systems. This paper gives an in depth review of creep mechanisms in Mg alloys and provides insight into alloy design principles for further development of creep performance in Mg.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".