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Record W4283712588 · doi:10.4028/p-3027xi

Potential Al-Fe Cast Alloys for Motor Applications in Electric Vehicles: An Overview

2022· article· en· W4283712588 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 · 2022
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCastabilityAutomotive industryMaterials scienceAluminiumMetallurgyMicrostructureElectric motorMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Iron is the main component of the earth's core, the most abundant element on the earth (about 35%), and it is relatively high in the sun and other stars. Also, it is a common and cheap metal in the manufacturing industry. Recently, with the rapid development of electric vehicles, more and more automotive companies are willing to develop new lightweight material for electric motors used in electrical vehicles. The iron–containing aluminum alloys can be considered as a good candidate, due to its great strength and electricity performance. This review describes various properties of aluminum-iron alloys including mechanical properties and electrical conductivities, as well their relation to the Fe contents. Also, metallurgical aspects of aluminum-iron alloys, including phase diagrams, equilibrium and non-equilibriun solidification, microstructure development, and castability. The further research and development work are outlined in terms of developing aluminum-iron alloys for some potential and value-added automotive 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 categoriesMeta-epidemiology (narrow)
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 score0.087
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)0.0000.000

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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