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Record W2891035654 · doi:10.1080/02670836.2018.1519945

Precipitation of iron-rich intermetallics and mechanical properties of Al–Si–Mg–Fe alloys with Al–5Ti–B

2018· article· en· W2891035654 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

VenueMaterials Science and Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceIntermetallicPrecipitationMicrostructureMetallurgyPorosityUltimate tensile strengthScanning electron microscopeAlloyComposite material

Abstract

fetched live from OpenAlex

Effects of added Al–5Ti–B master alloys on precipitation of iron-rich intermetallics and mechanical properties of A356 cast alloys with high Fe content (1.5 wt-%) were investigated using image analysis, scanning electron microscopy, and tensile testing. Results show that added Al–5Ti–B has apparent refinement on α (Al) grain size of A356 alloys that have high Fe content. 12 wt-% Al–5Ti–B is beneficial for improving mechanical properties of A356 cast alloys with high Fe content. Improved mechanical properties can be attributed to refined microstructure, the proper amounts of TiB 2 and Ti(AlSi) 3 , and decreased porosity. An excessive amount of Al–5Ti–B deteriorates mechanical properties of alloys because it leads to the formation of large secondary intermetallics and increased porosity.

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 score0.003
Threshold uncertainty score0.871

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 studies0.0000.002
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 head0.009
GPT teacher head0.202
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