Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In the past decade, magnesium (aluminum) alloy use in the automotive industry has increased in order to reduce vehicle weight and fuel consumption. However, their applications are usually limited to temperatures of up to 120°C. Improvements in the high-temperature mechanical properties of magnesium alloys would greatly expand their industrial applications. As compared to the unreinforced monolithic metal, metal matrix composites have been recognized to possess superior mechanical properties, such as high elastic modulus and strengths as well as enhanced wear resistance. In this study, a novel approach of making hybrid preforms with two or more types reinforcements, i.e., different size particles and fibers, for magnesium-based composites was developed. An advanced and affordable technique of fabricating hybrid magnesium-based composites called the preform-squeeze casting was employed successfully. The microstructures of the hybrid preform and the reinforced magnesium-based composites were analyzed with both optical microscopy (OM), and scanning electron microscopy (SEM). The microstructural analyses show that both the particles and fibers distribute homogeneously in the preform and the composite's matrix material. The engineering performance of the composites was evaluated by tensile testing both the reinforced and unreinforced matrix alloy. The tensile testing results indicate that the tensile properties of the composites are considerably improved compared with those of the matrix magnesium alloy.</div></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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".