MétaCan
Menu
Back to cohort
Record W2089015810 · doi:10.1002/jbm.a.32205

Influence of surface modification on the <i>in vitro</i> corrosion rate of magnesium alloy AZ31

2008· article· en· W2089015810 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 Biomedical Materials Research Part A · 2008
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsLaurentian University
Fundersnot available
KeywordsMaterials scienceCorrosionSimulated body fluidMagnesiumMagnesium alloySurface modificationCoatingMetallurgyAlloyDegradation (telecommunications)Composite materialChemical engineeringScanning electron microscope

Abstract

fetched live from OpenAlex

In recent years, magnesium alloys have been proposed as a new class of metallic bioabsorbable implant material. Unfortunately, the production of hydrogen gas and an increase in alkalinity are both by-products of the degradation process of these materials. This necessitates the development of magnesium alloys with controlled degradation rates. Furthermore, biocompatible coatings that can delay the onset of corrosion would ensure that the mechanical integrity of the implant remains intact in the early stages of healing. This article explores the influence of surface modification by biomimetic calcium phosphate coating, biodegradable polymer coatings, and acid etching on the corrosion rate of the AZ31 magnesium alloy in simulated body fluid. Our results indicate that all of these surface treatments have a positive impact on the corrosion rate of the material and that in the early stages of implantation it is possible to tailor the corrosion rate through an appropriate choice of surface treatment.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.080
GPT teacher head0.334
Teacher spread0.254 · 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