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Effect of nickel and vanadium on iron bearing intermetallic phases in AA 5657 simulated DC castings

2013· article· en· W1978321751 on OpenAlex
Z. Zhang, Gege Li, X. G. Chen

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

VenueMaterials Science and Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceIntermetallicVanadiumNickelIngotMetallurgyPhase (matter)MicrostructureCastingDiffractionAnalytical Chemistry (journal)

Abstract

fetched live from OpenAlex

The effect of Ni and V trace additions at various levels on the transition and distribution of Fe bearing intermetallic phases on AA 5657 was investigated with a direct chill casting simulator. Phase identification and quantification were performed using a combination of SEM, electron backscattered diffraction, energy dispersive spectrum and image analysis techniques to accurately evaluate the effect of Ni and V on ingot microstructures. The Ni addition promotes Al 3 Fe and α-AlFeSi while suppressing Al m Fe. At high Ni levels, a new Ni rich AlFeNi phase was observed. On the other hand, V strongly encourages Al m Fe and lowers the critical cooling rates of the phase formation. However, at low Ni or V levels (70–150 and 170–240 ppm respectively), the effect of Ni and V on phase selection is still weak and may not significantly affect the material properties. A counteractive effect of the combined Ni and V additions on the phase selection was observed.

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 score0.005
Threshold uncertainty score0.380

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 studies0.0000.001
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 head0.004
GPT teacher head0.201
Teacher spread0.197 · 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