Research advances in magnesium and magnesium alloys worldwide in 2020
Bibliographic record
Abstract
Research on magnesium alloys continues to attract great attention, with more than 3000 papers on magnesium and magnesium alloys published and indexed in SCI in 2020 alone. The results of bibliometric analyses show that microstructure control and mechanical properties of Mg alloys are continuously the main research focus, and the corrosion and protection of Mg alloys are still widely concerned. The emerging research hot spots are mainly on functional magnesium materials, such as Mg ion batteries, hydrogen storage Mg materials, and bio-magnesium alloys. Great contributions to the research and development of magnesium alloys in 2020 have been made by Chongqing University, Chinese Academy of Sciences, Central South University, Shanghai Jiaotong University, Northeastern University, Helmholtz Zentrum Geesthacht, etc. The directions for future research are suggested, including: 1) the synergistic control of microstructures to achieve high-performance magnesium alloys with concurrent high strength and superior plasticity along with high corrosion resistance and low cost; 2) further development of functional magnesium materials such as Mg batteries, hydrogen storage Mg materials, structural-functional materials and bio-magnesium materials; 3) studies on the effective corrosion protection and control of degradation rate of magnesium alloys; 4) further improvement of advanced processing technology on Mg alloys.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".