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In Vitro Bioactivity Assessment of Metallic Magnesium

2006· article· en· W2020590177 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

VenueKey engineering materials · 2006
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSimulated body fluidMagnesiumImmersion (mathematics)ApatiteAqueous solutionMaterials scienceDegradation (telecommunications)Nuclear chemistryMetalChemistryMetallurgyMineralogy

Abstract

fetched live from OpenAlex

In the aim to decrease the degradation rate of magnesium in simulated body fluid, pure magnesium was treated by two different routes, i) by soaking specimens in an HF aqueous solution at 30oC for 30 min and ii) by heating specimens at 345oC for 15 min. The treated samples were immersed in simulated body fluid (SBF) at 37oC for different periods of time. Samples with no treatment were also immersed in SBF. The magnesium released into the SBF, the weight loss of the specimens and the pH of SBF increased with time of immersion in all the cases. The heat treated samples showed a lower degradation rate and lower pH values. A substantial decrease of magnesium concentration in the SBF corresponding to the heat treated samples was also observed. However, the degradation rate of the heat treated samples remains being extremely high. On the other hand, a bonelike apatite layer was observed after only 3 days of immersion in SBF in all the cases. The thickness of this layer increased with time of immersion. Further research needs to be performed to decrease the degradation rate. However, these results indicate that magnesium is a highly potential bioactive material for biomedical applications.

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 categoriesInsufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score1.000

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)0.0010.000

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 head0.008
GPT teacher head0.222
Teacher spread0.215 · 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