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Record W2793458165 · doi:10.1139/cjp-2017-0739

Mg<sub>65</sub>Ni<sub>20</sub>Y<sub>15–<i>X</i></sub>Ag<sub><i>X</i></sub> (<i>X</i> = 1, 2, 3, 5) alloys prepared via atmosphere controlled induction system

2018· article· en· W2793458165 on OpenAlexfundvenueno aff
Celal Kurşun, Musa Göğebakan, Yunus Azaklı, Sezgin Cengiz, Hasan Eskalen, Mehmet Tarakçı, Yücel Gençer

Bibliographic record

VenueCanadian Journal of Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
FundersUniversité de Sherbrooke
KeywordsScanning electron microscopeVickers hardness testIngotAnalytical Chemistry (journal)DiffractionX-ray crystallographyMaterials scienceChemical compositionPhysicsMicrostructureMetallurgyCrystallographyComposite materialAlloyOpticsChemistry

Abstract

fetched live from OpenAlex

In this work, Mg 65 Ni 20 Y 15–X Ag X (X = 1, 2, 3, 5) alloys were manufactured by atmosphere controlled induction system. The effect of Ag ratio on the microstructural properties, micro-hardness, density, and homogeneity of the Mg–Ni–Y alloys were investigated. These alloys were characterized by X-ray diffraction (XRD), optical microscopy, scanning electron microscopy with energy dispersive X-ray (SEM-EDX) and Vickers micro-hardness measurement. According to XRD results, Ni 3 Y and Mg 6 Ni phases were observed as well as AgY and Ag 17 Mg 54 phases, which were obtained in alloys. The quantitative results of EDX analysis confirm that the chemical composition of the obtained phases is very close and their homogeneities are so high. The average micro-hardness values of the ingot alloys were measured between 208 and 266 HV for matrix. The elastic modulus and densities of the Mg 65 Ni 20 Y 15–X Ag X (X = 1, 2, 3, 5) alloys increased by increasing Ag in the alloys and they were determined in the range of 58.18–68.12 GPa and 3.14–3.53 g/cm 3 , respectively.

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 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), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2018
Admission routes2
Has abstractyes

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