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Overview of Key R&D Activities for the Development of High-Performance Magnesium Materials in Canada

2005· article· en· W2087875438 on OpenAlexaffabout
J. A. Jackman, Jeffrey Wood, Elhachmi Essadiqi, Jason Lo, Mahi Sahoo, Su Xu, J.P. THOMSON, Wei Jie Liu

Bibliographic record

VenueMaterials science forum · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsMaterials scienceExcellenceAutomotive industryMetallurgyWork (physics)Research developmentHigh pressureManufacturing engineeringMechanical engineeringEngineeringEngineering physicsPolitical science

Abstract

fetched live from OpenAlex

Canadian researchers are actively engaged in the development of novel cast, wrought and composite materials that are based on Mg. An overview is provided of Canadian research projects for new applications of Mg alloys that are targeted to the growing needs of the automotive sector. The research work described is funded primarily through two federal programs: the Canadian Lightweight Materials Research Initiative, and the Materials and Manufacturing Theme of the AUTO21 Network of Centres of Excellence. It includes work on mechanical and corrosion performance of high-pressure die castings, gravity and low pressure castings using permanent and sand molds, sheet Mg development and magnesium matrix composites. The metallurgical research facilities at the CANMET Materials Technology Laboratory are featured.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.014
Science and technology studies0.0060.001
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.004

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 head0.032
GPT teacher head0.253
Teacher spread0.221 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2005
Admission routes2
Has abstractyes

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