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Record W3161461929 · doi:10.1016/j.jma.2021.04.001

Research advances in magnesium and magnesium alloys worldwide in 2020

2021· article· en· W3161461929 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Magnesium and Alloys · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsToronto Metropolitan University
FundersNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectChongqing Research Program of Basic Research and Frontier TechnologyState Administration of Foreign Experts AffairsMinistry of Education of the People's Republic of China
KeywordsMagnesiumMaterials scienceCorrosionMicrostructureMetallurgy

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.298
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it