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Protective coatings for mechanical aluminum–magnesium joints

2015· article· en· W1986382402 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSurface Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCorrosionMetallurgyCoatingAlloyMicrostructureMagnesium alloyAluminiumConversion coatingGas dynamic cold spray5005 aluminium alloyAlonizing6111 aluminium alloyComposite material

Abstract

fetched live from OpenAlex

AZ31 alloy is used as a lightweight material for structural application in the automobile and aircraft production. However, alloy AZ31 is known to have poor corrosion resistant due to high electrochemical activity. In this study, the possibility of improving the corrosion resistance by applying protective coatings deposited by the low pressure cold spray process was investigated. The relative performance of each cold sprayed corrosion preventive coatings was assessed in accordance with American Society for Testing and Materials standards. The data for the bare AZ31 alloy were initially obtained and used as a reference point to compare the corrosion protective performance of different preventive coatings. Electrochemical behaviour of each coating composition was analyzed after a given time period of the accelerated corrosion test. Microstructure and mechanical properties of the deposited preventive coatings are also discussed. The cold sprayed preventive coatings provide sufficient protection to substantially reduce the corrosion rate of alloy AZ31.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.234
Teacher spread0.206 · 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