Magnesium Matrix Composites for Elevated Temperature Applications
Bibliographic record
Abstract
<div class="htmlview paragraph">Recently, there have been substantial activities focusing on the development of magnesium alloys for high temperature applications (&gt;150°C) through alloying additions, in order to expand its use. However, a simple route of improving the elevated temperature property of a magnesium alloy through the composite route has been overlooked. In particularly, the possibility of reinforcing a magnesium component through selective reinforcement has not been widely explored.</div> <div class="htmlview paragraph">In this paper, a viable technique of fabricating magnesium matrix composites through squeeze casting is provided. In addition, the novel approach of making preforms and the manner which they can be selectively reinforced into magnesium components is introduced. Finally, results on some of the key properties of magnesium composites developed by this technique along with the characterization results are presented.</div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".